“…The clinical implications of this study are that the Bronze model supports accessible detection of SAD in asthma in primary care where access to respiratory tests is often limited (16,17). Although SAD may be present across all severities of asthma, it is particularly present in more severe asthma (11).…”
Section: Discussionmentioning
confidence: 77%
“…Moreover, the diagnostic ability of the Bronze model was comparable to other frequently used tests in primary care, e.g. fractional exhaled nitric oxide (FeNo) in diagnosing asthma (21) or the nitrite test in diagnosing urine tract infections (17,18).…”
Section: Discussionmentioning
confidence: 85%
“…Our study showed that a simple tool including one SADT-item and a few basic patient parameters which are always available may reasonably predict SAD in asthma patients (Bronze model). This is especially relevant for primary care settings where in many countries access to spirometry is limited (16,17). Even in European counties, a considerable part of primary care practices does not perform lung function tests and when they do, formal accredited training rates were found to be low (18)(19)(20).…”
BackgroundSmall airways dysfunction (SAD) in asthma is difficult to measure and a gold standard is lacking. The aim of this study was to develop a simple tool including items of the small airways dysfunction tool (SADT) questionnaire, basic patient characteristics and respiratory tests available depending on clinical setting, to predict SAD in asthma.MethodsThis study was based on the data of the multinational ATLANTIS (Assessment of Small Airways Involvement in Asthma) study including the earlier developed SADT questionnaire. Key SADT-items together with clinical information was now used to build logistic regression models to predict SAD group (less likely or more likely to have SAD). Diagnostic ability of the models was expressed as area under the receiver operating characteristic curve (AUC) and positive likelihood ratios (LR+).ResultsSADT-item 8, “I sometimes wheeze when I am sitting or lying quietly”, and the patient characteristics age, age at asthma diagnosis and BMI could reasonably well detect SAD (AUC:0.74, LR+:2.3). The diagnostic ability increased by adding spirometry (FEV1pp; AUC:0.87, LR+:5.0) and oscillometry (R5-R20 and AX; AUC:0.96, LR+:12.8).ConclusionIf access to respiratory tests is limited (e.g.primary care in many countries), patients with SAD could reasonably well be identified by asking about wheezing at rest and a few patient characteristics. In (advanced) hospital settings patients with SAD could be identified with considerably higher accuracy using spirometry and oscillometry.
“…The clinical implications of this study are that the Bronze model supports accessible detection of SAD in asthma in primary care where access to respiratory tests is often limited (16,17). Although SAD may be present across all severities of asthma, it is particularly present in more severe asthma (11).…”
Section: Discussionmentioning
confidence: 77%
“…Moreover, the diagnostic ability of the Bronze model was comparable to other frequently used tests in primary care, e.g. fractional exhaled nitric oxide (FeNo) in diagnosing asthma (21) or the nitrite test in diagnosing urine tract infections (17,18).…”
Section: Discussionmentioning
confidence: 85%
“…Our study showed that a simple tool including one SADT-item and a few basic patient parameters which are always available may reasonably predict SAD in asthma patients (Bronze model). This is especially relevant for primary care settings where in many countries access to spirometry is limited (16,17). Even in European counties, a considerable part of primary care practices does not perform lung function tests and when they do, formal accredited training rates were found to be low (18)(19)(20).…”
BackgroundSmall airways dysfunction (SAD) in asthma is difficult to measure and a gold standard is lacking. The aim of this study was to develop a simple tool including items of the small airways dysfunction tool (SADT) questionnaire, basic patient characteristics and respiratory tests available depending on clinical setting, to predict SAD in asthma.MethodsThis study was based on the data of the multinational ATLANTIS (Assessment of Small Airways Involvement in Asthma) study including the earlier developed SADT questionnaire. Key SADT-items together with clinical information was now used to build logistic regression models to predict SAD group (less likely or more likely to have SAD). Diagnostic ability of the models was expressed as area under the receiver operating characteristic curve (AUC) and positive likelihood ratios (LR+).ResultsSADT-item 8, “I sometimes wheeze when I am sitting or lying quietly”, and the patient characteristics age, age at asthma diagnosis and BMI could reasonably well detect SAD (AUC:0.74, LR+:2.3). The diagnostic ability increased by adding spirometry (FEV1pp; AUC:0.87, LR+:5.0) and oscillometry (R5-R20 and AX; AUC:0.96, LR+:12.8).ConclusionIf access to respiratory tests is limited (e.g.primary care in many countries), patients with SAD could reasonably well be identified by asking about wheezing at rest and a few patient characteristics. In (advanced) hospital settings patients with SAD could be identified with considerably higher accuracy using spirometry and oscillometry.
“…The mixed urban/rural state of Selangor was chosen as it has a high prevalence of adults with asthma (22%) [ 5 ] as well as the highest prevalence of limited health literacy in Malaysia at 75% [ 7 , 25 ]. The selected clinics had a cohort of asthma patients registered in the Klang Asthma Cohort [ 26 , 27 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Patients were recruited from the Klang Asthma Cohort (NMRR-18–2707-42719) [ 26 , 27 ]. The registry is a research output of the NIHR Respiratory Global Health Unit (RESPIRE) in Malaysia [ 26 , 27 ].…”
Background
Digital technology tailored for those with limited health literacy has the potential to reduce health inequalities. Although mobile apps can support self-management in chronic diseases, there is little evidence that this approach applies to people with limited health literacy. We aimed to determine the acceptability of a self-management app in adults living with asthma and have limited health literacy and the feasibility of delivering the intervention and assessing outcomes.
Methods
We recruited eligible adults from the Klang Asthma Cohort registry in primary care for a 3-month mixed-method study plus a 2-month extended observation. We collected baseline data on socio-demography, health literacy and asthma control level. The outcomes of the intervention were assessed at 1- and 3-month: i) adoption (app download and usage), ii) adherence (app usage), iii) retention (app usage in the observation period), iv) health outcomes (e.g., severe asthma attacks) and v) process outcomes (e.g., ownership and use of action plans). At 1-month, participants were purposively sampled for in-depth interviews, which were audio-recorded, transcribed verbatim, and analysed deductively.
Results
We recruited 48 participants; 35 participants (23 Female; median age = 43 years; median HLS score = 28) completed the 3 months study. Of these, 14 participants (10 Female; median age = 48 years; median HLS score = 28) provided interviews. Thirty-seven (77%) participants adopted the app (downloaded and used it in the first month of the study). The main factor reported as influencing adoption was the ease of using the app. A total of 950 app usage were captured during the 3-month feasibility study. App usage increased gradually, peaking at month 2 (355 total log-ins) accounting for 78% of users. In month 5, 51.4% of the participants used the app at least once. The main factors influencing continued use included adherence features (e.g., prompts and reminders), familiarity with app function and support from family members.
Conclusions
An asthma self-management app intervention was acceptable for adults with limited health literacy and it was feasible to collect the desired outcomes at different time points during the study. A future trial is warranted to estimate the clinical and cost-effectiveness of the intervention and to explore implementation strategies.
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