Dataset Information

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

ABSTRACT: Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence.

SUBMITTER: Taylor TE

PROVIDER: S-EPMC5773205 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

Taylor Terence E TE Lacalle Muls Helena H Costello Richard W RW Reilly Richard B RB

PloS one 20180118 1

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple c ...[more]

PMID: 29346430

Similar Datasets

Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Project description:Suboptimal adherence to maintenance therapy contributes to poor asthma control and exacerbations. This study evaluated the effect of different elements of a connected inhaler system (CIS), comprising clip-on inhaler sensors, a patient-facing app and a healthcare professional (HCP) dashboard, on adherence to asthma maintenance therapy.This was an open-label, parallel-group, 6-month, randomised controlled trial in adults with uncontrolled asthma (asthma control test (ACT) score less than 20) on fixed-dose inhaled corticosteroids/long-acting β-agonist maintenance therapy (n=437). All subjects received fluticasone furoate/vilanterol ELLIPTA dry-powder inhalers for maintenance and salbutamol/albuterol metered-dose inhalers for rescue, with a sensor attached to each inhaler. Participants were randomised to one of five CIS study arms (allocation ratio 1:1:1:1:1) reflecting the recipient of the data feedback from the sensors, as follows: 1) maintenance use to participants and HCPs (n=87); 2) maintenance use to participants (n=88); 3) maintenance and rescue use to participants and HCPs (n=88); 4) maintenance and rescue use to participants (n=88); and 5) no feedback (control) (n=86).For the primary endpoint, observed mean±sd adherence to maintenance therapy over months 4-6 was 82.2±16.58% (n=83) in the "maintenance to participants and HCPs" arm and 70.8±27.30% (n=85) in the control arm. The adjusted least squares mean±se was 80.9±3.19% and 69.0±3.19%, respectively (study arm difference: 12.0%, 95% CI 5.2-18.8%; p<0.001). Adherence was also significantly greater in the other CIS arms versus the control arm. The mean percentage of rescue medication free days (months 4-6) was significantly greater in participants receiving data on their rescue use compared with controls. ACT scores improved in all study arms with no significant differences between groups.A CIS can improve adherence to maintenance medication and reduce rescue medication use in patients with uncontrolled asthma.

Project description:BackgroundMedication nonadherence is a global public health challenge that results in suboptimal health outcomes and increases health care costs. Forgetting to take medicines is one of the most common reasons for unintentional medication nonadherence. Research findings indicate that voice-activated virtual home assistants, such as Amazon Echo and Google Home devices, may be useful in promoting medication adherence.ObjectiveThis study aims to create a medication adherence app (skill), MedBuddy, for Amazon Echo devices and measure the use, usability, and usefulness of this medication-taking reminder skill.MethodsA single-group, mixed methods, cohort feasibility study was conducted with women who took oral contraceptives (N=25). Participants were undergraduate students (age: mean 21.8 years, SD 6.2) at an urban university in the Southeast United States. Participants were given an Amazon Echo Dot with MedBuddy-a new medication reminder skill for Echo devices created by our team-attached to their study account, which they used for 60 days. Participants self-reported their baseline and poststudy medication adherence. MedBuddy use was objectively evaluated by tracking participants' interactions with MedBuddy through Amazon Alexa. The usability and usefulness of MedBuddy were evaluated through a poststudy interview in which participants responded to both quantitative and qualitative questions.ResultsParticipants' interactions with MedBuddy, as tracked through Amazon Alexa, only occurred on half of the study days (mean 50.97, SD 29.5). At study end, participants reported missing their medication less in the past 1 and 6 months compared with baseline (χ21=0.9 and χ21=0.4, respectively; McNemar test: P<.001 for both). However, there was no significant difference in participants' reported adherence to consistently taking medication within the same 2-hour time frame every day in the past 1 or 6 months at the end of the study compared with baseline (χ21=3.5 and χ21=0.4, respectively; McNemar test: P=.63 and P=.07, respectively). Overall feedback about usability was positive, and participants provided constructive feedback about the skill's features that could be improved. Participants' evaluation of MedBuddy's usefulness was overwhelmingly positive-most (15/23, 65%) said that they would continue using MedBuddy as a medication reminder if provided with the opportunity and that they would recommend it to others. MedBuddy features that participants enjoyed were an external prompt separate from their phone, the ability to hear the reminder prompt from a separate room, multiple reminders, and verbal responses to prompts.ConclusionsThe findings of this feasibility study indicate that the MedBuddy medication reminder skill may be useful in promoting medication adherence. However, the skill could benefit from further usability enhancements.

Project description:BackgroundInhaler selection is important when managing respiratory conditions; a patient's inhalation technique should be appropriate for the selected device, and patients should ideally be able to use a device successfully regardless of disease severity. The NEXThaler is a multidose dry-powder inhaler with a breath-actuated mechanism (BAM) and dose counter that activates only following inhalation, so effectively an 'inhalation counter'. We assessed inspiratory flow through the NEXThaler in two studies and examined whether inhalation triggered the BAM.MethodsThe two studies were open-label, single-arm, and single visit. One study recruited patients with asthma aged???18 years; the other recruited patients with chronic obstructive pulmonary disease (COPD) aged???40 years. All patients inhaled twice through a placebo NEXThaler. The inspiratory profile through the device was assessed for each inhalation using acoustic monitoring, with flow at and time to BAM firing, peak inspiratory flow (PIF), and total inhalation time assessed.ResultsA total of 40 patients were enrolled in the asthma study: 20 with controlled asthma and 20 with partly controlled/uncontrolled asthma. All patients were able to trigger the BAM, as evidenced by the inhalation counter activating on closing the device. Mean flow at BAM firing following first inhalation was 35.0 (range 16.3-52.3) L/min; mean PIF was 64.6 (35.0-123.9) L/min. A total of 72 patients were enrolled in the COPD study, with data analysed for 69 (mean forced expiratory volume in 1 s 48.7% predicted [17-92%]). As with the asthma study, all patients, regardless of airflow limitation, were able to trigger the BAM. Mean flow at BAM firing following first inhalation was 41.9 (26.6-57.1) L/min; mean PIF was 68.0 (31.5-125.4) L/min. Device usability was rated highly in both studies, with 5 min sufficient to train the patients, and a click heard shortly after inhalation in all cases (providing feedback on BAM firing).ConclusionsInhalation flows triggering the BAM in the NEXThaler were similar between patients with controlled and partly controlled/uncontrolled asthma, and were similar across COPD airflow limitation. All enrolled patients were able to activate the device.

Project description:BACKGROUND: A standard short-term inhalation study (STIS) was applied for hazard assessment of 13 metal oxide nanomaterials and micron-scale zinc oxide. METHODS: Rats were exposed to test material aerosols (ranging from 0.5 to 50 mg/m3) for five consecutive days with 14- or 21-day post-exposure observation. Bronchoalveolar lavage fluid (BALF) and histopathological sections of the entire respiratory tract were examined. Pulmonary deposition and clearance and test material translocation into extra-pulmonary organs were assessed. RESULTS: Inhaled nanomaterials were found in the lung, in alveolar macrophages, and in the draining lymph nodes. Polyacrylate-coated silica was also found in the spleen, and both zinc oxides elicited olfactory epithelium necrosis. None of the other nanomaterials was recorded in extra-pulmonary organs. Eight nanomaterials did not elicit pulmonary effects, and their no observed adverse effect concentrations (NOAECs) were at least 10 mg/m3. Five materials (coated nano-TiO2, both ZnO, both CeO2) evoked concentration-dependent transient pulmonary inflammation. Most effects were at least partially reversible during the post-exposure period.Based on the NOAECs that were derived from quantitative parameters, with BALF polymorphonuclear (PMN) neutrophil counts and total protein concentration being most sensitive, or from the severity of histopathological findings, the materials were ranked by increasing toxic potency into 3 grades: lower toxic potency: BaSO4; SiO2.acrylate (by local NOAEC); SiO2.PEG; SiO2.phosphate; SiO2.amino; nano-ZrO2; ZrO2.TODA; ZrO2.acrylate; medium toxic potency: SiO2.naked; higher toxic potency: coated nano-TiO2; nano-CeO2; Al-doped nano-CeO2; micron-scale ZnO; coated nano-ZnO (and SiO2.acrylate by systemic no observed effect concentration (NOEC)). CONCLUSION: The STIS revealed the type of effects of 13 nanomaterials, and micron-scale ZnO, information on their toxic potency, and the location and reversibility of effects. Assessment of lung burden and material translocation provided preliminary biokinetic information. Based upon the study results, the STIS protocol was re-assessed and preliminary suggestions regarding the grouping of nanomaterials for safety assessment were spelled out.

Dataset Information

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

Publications

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets