Exploring Smartphone App Use in Older Adults with a Chronic Respiratory Disease: The biggest Problem I have Is I Don't Understand My Phone At All.

INTRODUCTION: In older people with a chronic respiratory disease, we explored (i) usual Smartphone application (App) use, (ii) the time taken to download and use an App, and (iii) changes in self-efficacy for downloading an App after a single practice session. METHODS: Participants were invited to attend one or two separate assessment sessions (Part A and B). Those who attended Part A had data pertaining to their App usage over the previous week extracted from their Smartphone. Those who attended Part B were asked to download and use a pedometer App and "think out loud" during the task. Before and after the task, participants rated their self-efficacy for downloading an App using a Visual Analogue Scale (0-10). RESULTS: Twenty-seven participants (mean ± SD 74 ± 5 years) completed Part A. Commonly used Apps related to communication (e.g., texting; median [interquartile range] 15 [9-25] min/day) and interest (e.g., news; 14 [4-50] min/day). Fifteen participants completed Part B (mean ± SD 73 ± 7 years). The median time taken to download and use the App was 24 (22-37) min. The "think out loud" data converged into four domains: (i) low self-efficacy for using and learning Apps; (ii) reliance on others for help; (iii) unpleasant emotional responses; and (iv) challenges due to changes associated with longevity. Self-efficacy increased by 4 (95% confidence interval: 3-6). CONCLUSION: This population used Apps mainly to facilitate social connection. It took participants almost half an hour to download and use an App, but a single practice session improved self-efficacy.

A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study.

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm's positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm's performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Characterisation of the Australian Adult Population Living with Asthma: Severe - Exacerbation Frequency, Long-Term OCS Use and Adverse Effects.

Introduction: Asthma poses a significant burden for the Australian population. Understanding severe exacerbation rates, and steroid-related burden for adults diagnosed with asthma stands to offer insights into how this could be reduced. Methods: Electronic medical records (EMR) and questionnaires from the Optimum Patient Care Research Database Australia (OPCRDA) were utilised retrospectively. OPCRDA is a real-world database with >800,000 medical records from Australian primary care practices. Outcomes were severe asthma exacerbations in Australian adults, over a 12-month period, stratified by Global Initiative for Asthma (GINA) treatment intensity steps, and steroid associated comorbidities. Results: Of the 7868 adults treated for asthma, 19% experienced at least one severe exacerbation in the last 12-months. Severe exacerbation frequency increased with treatment intensity (≥1 severe exacerbation GINA 1 13%; GINA 4 23%; GINA 5a 33% and GINA 5b 28%). Questionnaire participants reported higher rates of severe exacerbations than suggested from their EMR (32% vs 23%) especially in steps 1, 4 and 5. Patients repeatedly exposed to steroids had an increased risk of osteoporosis (OR 1.95, 95% CI 1.43-2.66) and sleep apnoea (OR 1.78, 95% CI 1.30-2.46). Conclusion: The Australian population living with GINA 1, 4, 5a and 5b asthma have high severe exacerbation rates and steroid-related burden, especially when compared to other first world countries, with these patients needing alternative strategies or possibly specialist assessment to better manage their condition.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9-89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4-96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0-87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations.

BACKGROUND: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations. AIM: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs. DESIGN AND SETTING: A prospective cohort study using data from participants aged >12 years presenting with acute respiratory symptoms to a hospital in Western Australia. METHOD: Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, and age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP. Independent cohorts were recruited to train and test the accuracy of the algorithm. Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians. Specialist radiologists reported medical imaging. RESULTS: The smartphone-based algorithm had high percentage agreement (PA) with the clinical diagnosis of CAP in the total cohort (n = 322, positive PA [PPA] = 86.2%, negative PA [NPA] = 86.5%, area under the receiver operating characteristic curve [AUC] = 0.95); in participants 22-<65 years (n = 192, PPA = 85.7%, NPA = 87.0%, AUC = 0.94), and in participants aged ≥65 years (n = 86, PPA = 85.7%, NPA = 87.5%, AUC = 0.94). Agreement was preserved across CAP severity: 85.1% (n = 80/94) of participants with CRB-65 scores 1 or 2, and 87.7% (n = 57/65) with a score of 0, were correctly diagnosed by the algorithm. CONCLUSION: The algorithm provides rapid and accurate diagnosis of CAP. It offers improved accuracy over current protocols when clinical evaluation is difficult. It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.

Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study.

BACKGROUND: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. OBJECTIVE: The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. METHODS: Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. RESULTS: The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. CONCLUSIONS: The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939.

Corrigendum: Predicting spirometry readings using cough sound features and regression (2018 Physiol. Meas. 39 095001).

The purpose of this submission is to provide missing information to complete the conflict of interest statement associated with the article. The statements provided here augment the already provided information rather than replace it.

Predicting spirometry readings using cough sound features and regression.

OBJECTIVE: Spirometry is a commonly used method of measuring lung function. It is useful in the definitive diagnosis of diseases such as asthma and chronic obstructive pulmonary disease (COPD). However, spirometry requires cooperative patients, experienced staff, and repeated testing to ensure the consistency of measurements. There is discomfort associated with spirometry and some patients are not able to complete the test. In this paper, we investigate the possibility of using cough sound analysis for the prediction of spirometry measurements. APPROACH: Our approach is based on the premise that the mechanism of cough generation and the forced expiratory maneuver of spirometry share sufficient similarities enabling this prediction. Using an iPhone, we collected mostly voluntary cough sounds from 322 adults presenting to a respiratory function laboratory for pulmonary function testing. Subjects had the following diagnoses: obstructive, restrictive, or mixed pattern diseases, or were found to have no lung disease along with normal spirometry. The cough sounds were automatically segmented using the algorithm described in Sharan et al (2018 IEEE Trans. Biomed. Eng.). We then represented cough sounds with various cough sound descriptors and built linear and nonlinear regression models connecting them to spirometry parameters. Augmentation of cough features with subject demographic data is also experimented with. The dataset was divided into 272 training subjects and 50 test subjects for experimentation. MAIN RESULTS: The performance of the auto-segmentation algorithm was evaluated on 49 randomly selected subjects from the overall dataset with a sensitivity and PPV of 84.95% and 98.51%, respectively. Our regression models achieved a root mean square error (and correlation coefficient) for standard spirometry parameters FEV1, FVC, and FEV1/FVC of 0.593L (0.810), 0.725L (0.749), and 0.164 (0.547), respectively, on the test dataset. In addition, we could achieve sensitivity, specificity, and accuracy of 70% or higher by applying the GOLD standard for COPD diagnosis on the estimated spirometry test results. SIGNIFICANCE: The experimental results show high positive correlation in predicting FEV1 and FVC and moderate positive correlation in predicting FEV1/FVC. The results show possibility of predicting spirometry results using cough sound analysis.