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In recent years, mobile health (mHealth)–related apps have been developed to help manage chronic diseases. Apps may allow patients with a chronic disease characterized by exacerbations, such as chronic obstructive pulmonary disease (COPD), to track and even suspect disease exacerbations, thereby facilitating self-management and prompt intervention. Nevertheless, there is insufficient evidence regarding patient compliance in the daily use of mHealth apps for chronic disease monitoring.
This study aimed to provide further evidence in support of prospectively recording daily symptoms as a useful strategy to detect COPD exacerbations through the smartphone app, Prevexair. It also aimed to analyze daily compliance and the frequency and characteristics of acute exacerbations of COPD recorded using Prevexair.
This is a multicenter cohort study with prospective case recruitment including 116 patients with COPD who had a documented history of frequent exacerbations and were monitored over the course of 6 months. At recruitment, the Prevexair app was installed on their smartphones, and patients were instructed on how to use the app. The information recorded in the app included symptom changes, use of medication, and use of health care resources. The patients received messages on healthy lifestyle behaviors and a record of their cumulative symptoms in the app. There was no regular contact with the research team and no mentoring process. An exacerbation was considered reported if medical attention was sought and considered unreported if it was not reported to a health care professional.
Overall, compliance with daily records in the app was 66.6% (120/180), with a duration compliance of 78.8%, which was similar across disease severity, age, and comorbidity variables. However, patients who were active smokers, with greater dyspnea and a diagnosis of depression and obesity had lower compliance (
The daily use of the Prevexair app is feasible and acceptable for patients with COPD who are motivated in their self-care because of frequent exacerbations of their disease. Monitoring through the Prevexair app showed great potential for the implementation of self-care plans and offered a better diagnosis of their chronic condition.
Chronic obstructive pulmonary disease (COPD) places an enormous burden on health care systems. A substantial proportion of the cost is attributable to hospitalizations, mostly owing to acute exacerbations of respiratory symptoms [
Knowledge about COPD exacerbation frequency is important to assess the clinical risk [
In recent years, health-related apps running on mobile devices such as smartphones and tablets, known as mobile health (mHealth) apps, have been developed to help manage chronic diseases [
This study aimed to provide further evidence in support of the hypothesis that prospectively recording daily symptoms is a useful tool to monitor and help correctly assess COPD exacerbations and the clinical risk of COPD based on the patient’s daily self-reporting of symptoms using Prevexair, a simple smartphone app, in which the patient records their daily symptoms and which offers general recommendations. As there is not enough information based on previously reported results, this study was proposed as a pilot study to generate information.
This paper has provided data about compliance in the daily use of a mHealth app for the long-term monitoring of patients with COPD without a mentoring process or regular phone calls. In addition, this paper has analyzed the frequency and characteristics of AECOPD recorded via the smartphone app, Prevexair.
Patients were recruited in outpatient respiratory clinics from 6 tertiary referral hospitals in Spain between November 2016 and March 2018. The inclusion criteria were as follows: aged above 40 years, having a history of smoking (≥10 pack-years), a diagnosis of COPD confirmed by postbronchodilator spirometry with a forced expiratory volume in one second (FEV1) to forced vital capacity ratio of less than 0.7 in the stable phase of the disease, having a history of at least two exacerbations treated with oral corticosteroids or antibiotics or having been hospitalized at least once for exacerbation in the past 12 months, owning a smartphone, and having the cognitive and motor ability to operate a smartphone. Patients were excluded if they had other significant respiratory diseases or if they reported an exacerbation during the run-in period. Ethical approval was obtained from the Ethics Committee at the Hospital Clínico San Carlos (Madrid, Spain; internal code 14/124-E), and all patients gave their written informed consent before inclusion.
This was a multicenter, prospective cohort study with a 2-week run-in period followed by a 6-month follow-up period. The study visits were scheduled as follows: before the 2-week run-in period (selection visit), after the run-in period (inclusion visit), during the follow-up period at 3 months (visit 1), and at 6 months (visit 2). The run-in period was used to make sure patients were stable. Patient assessment included a complete medical history (height, weight, smoking history, drug history, diagnosis of depression and/or anxiety, and other comorbid conditions), spirometry, health-related quality of life using the COPD Assessment Test (CAT), dyspnea using the modified Medical Research Council (mMRC) questionnaire, and the number of moderate/severe exacerbations in the last year.
The app was developed for iOS and Android systems by using Virtual Ware. The app is available for installation on a mobile device. At recruitment, during selection visit, the Prevexair app was installed on patients’ smartphones, and they were instructed how to use the app and received instructions to record their daily respiratory symptoms in the app, once under supervision. Thereafter, in the inclusion visit, their ability to use the app was reviewed. No problems were recorded regarding the use of the mobile app.
The information recorded in the app included symptom changes, use of medication, and use of health care resources.
Additional information gathered at each scheduled visit during a clinically stable period included changes in medication, current smoking habits, CAT, and use of health care resources. During the visit at 6 months, the level of satisfaction with the app was evaluated on a scale from 0 to 10. The medical staff at the visit were blinded and not informed about the Prevexair app records.
Screenshots of the Prevexair app in a smartphone: initial screens.
Screenshots of the Prevexair app in a smartphone: questionnaire.
To analyze data recording compliance in the app, there were 180
An exacerbation was defined as an increase in respiratory symptoms for 2 consecutive days, with at least one major symptom (dyspnea, sputum purulence, or sputum volume) and either another major or minor symptom (wheeze, cold, sore throat, or cough). The first day a symptom worsened was defined as the day of onset of the exacerbation, according to the previously validated criteria [
Exacerbation duration was defined as the number of days after onset that worsened symptoms persisted. The last day of recorded worsened symptoms before 2 consecutive symptom-free days was defined as the end of the exacerbation. Patients had to be symptom-free for ≥7 days before a new exacerbation onset was defined. Exacerbation recovery could not be determined if patients failed to record symptoms or continuously recorded symptoms for more than 30 days after onset.
An exacerbation was considered reported if medical attention was sought through scheduled or unscheduled doctor visits, emergency department visits, or hospital admissions. It was considered unreported if it was not medically reported. An exacerbation was considered treated if there was a change in at least one medication (ie, antibiotics, corticosteroids, or bronchodilators) for the worsened symptom.
To analyze the impact of exacerbation on the health status, the patients were categorized into four exclusive groups according to the reporting status for exacerbations: no exacerbation, unreported exacerbation only, reported exacerbation only, and mixed unreported (at least one) and reported (at least one) exacerbations. For this analysis, those patients with less than 60% overall compliance were excluded.
Qualitative variables were summarized by their frequency distribution and quantitative variables by their mean and standard deviation. Continuous nonnormally distributed variables were summarized by median and interquartile range (IQR P25-P75).
The association between the quantitative variable level of compliance and patient characteristics was evaluated with the nonparametric Mann-Whitney test for two independent groups or the Kruskal-Wallis test for more than two groups.
The association between each independent variable (baseline patient characteristics and event characteristics) and the dependent variable type of exacerbation (unreported or reported) was assessed by calculating the crude odds ratio via a multilevel logistic regression analysis. The multilevel analysis included two levels: the event level (level 1) and the patient level (level 2). A multivariable, multilevel logistic model was fitted to evaluate the independent effect of the selected variables. Candidate predictors with
In the study of the relationship between exacerbations and quality of life, quantitative variables were compared between the four groups in the study using the Kruskal-Wallis test and qualitative variables were compared using the chi-square test.
All analyses were performed using STATA 15.0 software (StataCorp LLC). Statistical significance was assumed at
Of the 126 patients recruited, 10 patients were excluded because they had one exacerbation at enrollment. A total of 116 patients were eligible for analysis; 21.6% (25/126) of participants were women and 13.8% (16/116) were active smokers. The baseline characteristics of the analyzed cohort are reported in
Baseline characteristics of the study population.
Characteristics | Values | |
Patients, n | 116 | |
Gender (male), n (%) | 91 (78.4) | |
Age (years), mean (SD) | 66.51 (8.14) | |
Active smokers, n (%) | 16 (13.8) | |
Smoking pack-years, mean (SD) | 44.1 (23.60) | |
|
||
|
Mean (SD) | 27.30 (4.96) |
|
≤21, n (%) | 9 (7.8) |
|
||
|
Mean (SD) | 2.62 (1.41) |
|
≥3, n (%) | 58 (50.0) |
|
||
|
0-1 | 27 (23.2) |
|
≥2 | 89 (76.7) |
CATb questionnaire, mean (SD) | 14.10 (6.13) | |
Chronic bronchitis, n (%) | 61 (52.6) | |
Chronic colonization, n (%) | 2 (1.7) | |
History of asthma, n (%) | 7 (6.0) | |
Post-BD FEV1 (%)c, mean (SD) | 44.62 (16.23) | |
Post-BD FEV1 (mL), mean (SD) | 1192.11 (477.16) | |
Number of severe-moderate exacerbationsd in the last year, median (IQR P25-75) | 3(2-3) | |
Number of moderate exacerbations in the last year ≥2, n (%) | 65 (56.0) | |
Number of severe exacerbations in the last year, median (IQR P25-75) | 1 (0-2) | |
Number of severe exacerbations in the last year ≥1, n (%) | 62 (53.4) | |
|
||
|
LAMAf monotherapy | 6 (5.2) |
|
LAMA-LABAg combination | 37 (31.9) |
|
LABA+ICSh combination | 6 (5.2) |
|
Triple therapyi | 67 (57.7) |
Long-term oxygen therapy, n (%) | 36 (31.0) | |
Chronic antibiotics, n (%) | 1 (0.9) |
amMRC: modified Medical Research Council.
bCAT: Chronic Obstructive Pulmonary Disease Assessment Test.
cPost-BD FEV1 %: postbronchodilator FEV1 percent predicted.
dSevere exacerbations refers to exacerbations requiring hospitalization; moderate exacerbations refer to exacerbations requiring outpatient management with antibiotics and/or corticosteroids systemic.
eCOPD: chronic obstructive pulmonary disease.
fLAMA: long-acting antimuscarinic agent.
gLABA: long-acting beta-2 agonist.
hICS: inhaled corticosteroid.
iTriple therapy: LABA+LAMA+ICS.
The 116 patients recorded data in the Prevexair app for a median of 178 (IQR 130-180) days, while the median number of records per patient was 120 (IQR 61-164).
Overall compliance in recording data daily in the app was 66.6% (120/180), with a compliance duration of 78.8%. Compliance is reported in
The percentage of patients who discontinued their use of the app was 6% during the first month and 8.6% during the second and third months, and 12.9% of patients abandoned the app during the last 3 months. The median (P25-75) level of satisfaction with the app was 10 (8-10).
Daily compliance by clinical variable.
Characteristics | Participants, n | Overall compliancea, median (IQR) | Compliance durationc, median (IQR) | |||
All subjects | 116 | 66.6 (33.8-91.1) | N/Ab | 78.8 (51.5-94.9) | N/A | |
|
.10 |
|
.03 | |||
|
Male | 91 | 71.6 (35.5-93.3) |
|
82.7 (56.7-95.6) |
|
|
Female | 25 | 53.3 (31.9-79.1) |
|
74.4 (42.1-85.2) |
|
|
.79 |
|
.88 | |||
|
<65 | 44 | 63.8 (34.8-90.5) |
|
78.1 (45.1-95.5) |
|
|
≥65 | 72 | 67.5 (33.8-91.9) |
|
79.4 (56.7-92.7) |
|
|
.02 |
|
.01 | |||
|
25-26.9 | 40 | 78.6 (42.2-95.4) |
|
80 (59.8-95.4) |
|
|
27-29.9 | 50 | 72.2 (42.9-92.2) |
|
85.7 (64.7-96.3) |
|
|
≥30 | 26 | 40.5 (11.2-75.5) |
|
54.5 (32.0-85.6) |
|
|
.03 |
|
.02 | |||
|
Active smoker | 16 | 40.5 (7.9 -72.6) |
|
54 (30.4-84.6) |
|
|
Former smoker | 100 | 71.6 (40.0-91.9) |
|
81.6 (56.7-95.0) |
|
|
.88 |
|
.37 | |||
|
<3 | 58 | 64.1 (40.6-89.7) |
|
75.3 (53.8-93.1) |
|
|
≥3 | 58 | 72.2 (31.1-92.3) |
|
84.2 (45.3-95.8) |
|
|
.57 |
|
.21 | |||
|
Presence | 61 | 64.4 (32.2-89.4) |
|
77.9 (44.9-92.0) |
|
|
Absence | 55 | 68.3 (39.4-95.0) |
|
85.5 (60.5-96.2) |
|
|
.86 |
|
.66 | |||
|
Presence | 12 | 59.1 (35.9-89.8) |
|
70.2 (55.0-90.1) |
|
|
Absence | 104 | 67.5 (33.8-91.1) |
|
80 (50.9-94.9) |
|
|
.57 |
|
.21 | |||
|
Presence | 61 | 64.4 (32.2-89.4) |
|
77.9 (44.9-92.0) |
|
|
Absence | 55 | 68.3 (39.4-95.0) |
|
85.5 (60.5-96.2) |
|
|
.08 |
|
.04 | |||
|
0-1 | 27 | 80.5 (45-98.8) |
|
87.9 (61.3-100) |
|
|
≥2 | 89 | 63.8 (32.2-90.5) |
|
75.6 (47.1-91.9) |
|
|
.76 |
|
.97 | |||
|
<10 | 31 | 61.6 (41.6-92.7) |
|
76.5 (50-96.7) |
|
|
≥10 | 84 | 70 (33.3-90.5) |
|
80.5 (54.5-92.6) |
|
|
.13 |
|
.34 | |||
|
Presence | 21 | 88.3(47.5-93.6) |
|
88.3 (52.3-94.3) |
|
|
Absence | 95 | 63.8 (33.3-90.5) |
|
75.7 (51.3-94.9) |
|
|
.008 |
|
.01 | |||
|
Presence | 7 | 12.2 (3.3-53.3) |
|
32.1 (9.2 -87.2) |
|
|
Absence | 109 | 71.6 (39.7-91.6) |
|
80.5 (56.6-95.0) |
|
|
.78 |
|
.65 | |||
|
≥50 | 37 | 71.6 (36.6-88.8) |
|
81.6 (46.5-91.8) |
|
|
<50 | 79 | 64.4 (33.3-92.2) |
|
77.9 (53.8-95.1) |
|
|
.30 |
|
.74 | |||
|
<2 | 11 | 51.6 (7.2- 86.6) |
|
80.6 (61.1-94.2) |
|
|
≥2 | 105 | 68.3 (37.5-91.1) |
|
78.8 (50.6-94.9) |
|
|
.42 |
|
.64 | |||
|
No triple therapy | 49 | 71.6 (47.2-90.0) |
|
78.8 (55.0-90.9) |
|
|
Triple therapyi | 67 | 66.6 (25.5-92.2) |
|
80.2 (45.0-95.1) |
|
|
.21 |
|
.10 | |||
|
Treated | 36 | 80.5 (33.1-95.0) |
|
87.1 (71.5-95.0) |
|
|
Not treated | 80 | 63.3 (33.8-90.2) |
|
73.6 (45.8-92.3) |
|
|
.10 |
|
.20 | |||
|
<10 | 50 | 63.8 (32.5-90.5) |
|
73.6 (47.5-90.8) |
|
|
≥10 | 54 | 78.6 (44.0-93.7) |
|
85.6 (55.4-96.2) |
|
aCompliance is expressed as median percentage (number of days completed/total number of days available for completion). Overall compliance: percentage of days in the entire study period (180 days) in which the app was used daily.
bNot applicable.
cCompliance duration: days elapsed from first daily entry to last, as the percentage of days available.
dmMRC: modified Medical Research Council.
eCAT: Chronic Obstructive Pulmonary Disease Assessment Test.
fPost-BD FEV1 %: postbronchodilator FEV1 percent predicted.
gSevere exacerbations refer to exacerbations requiring hospitalization; moderate exacerbations refer to exacerbations requiring outpatient management with antibiotics and/or corticosteroids systemic.
hCOPD: chronic obstructive pulmonary disease.
iTriple therapy: long-acting beta-2 agonists + long-acting antimuscarinic agents + inhaled corticosteroids.
During the study, patients experienced a total of 262 cases of symptom worsening, meeting the definition of exacerbation according to daily records in the app. The overall estimated rate of exacerbations recorded in the app was 2.25 (1.66) per person every 6 months. Of 116 patients, 18 (15.5%) had no events, 26 (22.4%) had one event, 25 (21.6%) patients had 2 events, and 47 (41.6%) patients had more than 2 events during the 6 months. Of 262 cases, 99 (37.8%) were reported exacerbations and 163 (62.2%) were unreported exacerbations.
Of the 163 unreported exacerbations, 76 (46.6%) were treated, but all were self-managed by the patient with an increase in bronchodilators in 57 events (35.0%), only antibiotics in 10 events (6.1%), and only oral corticosteroids in 9 (5.5%) events. Of the 99 reported exacerbations, all were treated: 15 (15%) events only with an increase in bronchodilators and the majority with only antibiotics (47/99, 47.5%) or with both oral corticosteroids and antibiotics (28/99, 28.3%). With regard to recorded health care utilization for exacerbation, 79.7% of exacerbations led to unscheduled contact, 2.2% led to emergency department visits, and 18.1% of exacerbations resulted in hospitalization.
Characteristics of unreported and reported exacerbations and the relationship between event characteristics and the likelihood of reporting an exacerbation.
Characteristics of exacerbations | Global | Unreported | Reported | Odds ratio (95% CI) | ||
Exacerbations, n (%) | 262 (100.0) | 163 (62.2) | 99 (37.8) | N/Aa | N/A | |
Duration of worsened symptoms, day median (P25-75) | 6 (4-9) | 5 (3-8) | 8 (6-11.2) | 1.17 (1.08-1.27) | <.001 | |
Total number of key symptoms, mean (SD) | 3.12 (1.09) | 2.91 (1.04) | 3.47 (1.09) | 1.88 (1.35-2.63) | <.001 | |
|
||||||
|
Dyspnea | 137 (52.3) | 87 (53.4) | 50 (50.5) | 0.95 (0.49-1.00) | .9 |
|
Sputum amount | 177 (67.6) | 106 (65.0) | 71 (71.7) | 1.48 (0.72-3.05) | .28 |
|
Sputum color | 91 (34.7) | 37 (22.7) | 54 (54.5) | 8.39 (3.31-21.22) | <.001 |
|
Cough | 172 (65.6) | 98 (60.1) | 74 (74.7) | 2.30 (1.13-4.66) | .02 |
|
Wheeze | 75 (28.6) | 45 (27.6) | 30 (30.3) | 1.31 (0.62-2.75) | .47 |
|
Sore throat | 57 (21.8) | 37 (22.7) | 20 (20.2) | 0.69 (0.29-1.63) | .49 |
|
Cold | 110 (41.9) | 65 (39.9) | 45 (45.5) | 1.30 (0.64-2.61) | .46 |
|
2.93 (1.25-6.85) | <.001 | ||||
|
2 | 85 (32.5) | 68 (41.7) | 17 (17.1) | ||
|
3 | 96 (36.6) | 60 (36.8) | 36 (36.4) | ||
|
4 or more | 81 (30.9) | 35 (21.5) | 46 (46.5) |
aNot applicable.
Baseline characteristics of patients by exacerbation category.
Characteristics | Unreported exacerbations | Reported exacerbations | Odds ratio (95% CI) | ||
Gender (male), n (%) | 123 (75.4) | 82 (83) | 1.61 (0.61-4.29) | .33 | |
|
|||||
|
Mean (SD) | 65.5 (8.7) | 67.6 (8.3) | 1.04 (0.58-4.68) | .08 |
|
≥65, n (%) | 102 (62.5) | 68 (69) | 1.60 (0.67-3.81) | .28 |
Active smokers, n (%) | 27 (16.6) | 12 (12) | 1.68 (0.53-5.26) | .37 | |
BMI (kg/m2), mean (SD) | 26.6 (5.0) | 26.8 (4.6) | 1.02 (0.94-1.11) | .55 | |
Number of comorbidities; mean (SD) ≥3, n (%) | 68 (41.7) | 55 (56) | 2.11 (0.94-4.73) | .07 | |
Depression, n (%) | 8 (4.9) | 3 (3) | 0.55 (0.07-3.97) | .56 | |
Anxiety, n (%) | 25 (15.3) | 26 (26) | 2.77 (0.99-7.70) | .05 | |
Dyspnea (mMRCa) ≥2, n (%) | 129 (79.1) | 87 (88) | 2.73 (0.88-8.41) | .08 | |
CATb questionnaire score ≥10, n (%) | 115 (70.5) | 80 (81) | 2.28 (0.88-5.94) | .09 | |
Post-BD FEV1 %c predicted <50%, n (%) | 114 (69.9) | 74 (75) | 1.58 (0.64-3.91) | .32 | |
Number of moderate-severe exacerbations in the last year ≥2, n (%) | 149 (91.4) | 96 (97) | 4.06 (0.69-23.72) | .12 |
amMRC: modified Medical Research Council.
bCAT: Chronic Obstructive Pulmonary Disease Assessment Test.
cPost-BD FEV1 %: postbronchodilator FEV1 percent predicted.
The relationship between exacerbation characteristics and the subject and the likelihood of reporting an exacerbation.
Characteristics | Odds ratio (95% CI) | ||
Duration of worsened symptoms | 1.15 (1.06-1.25) | <.001 | |
Mean number of key symptoms | 1.75 (1.20-2.56) | .003 | |
|
.45 | ||
|
≥3 | 1.50 (0.51-4.41) |
|
|
<3 | 1 |
|
|
.52 | ||
|
≥10 | 1.49 (0.44-5.00) |
|
|
<10 | 1 |
|
|
.42 | ||
|
≥2 | 1.75 (0.44-6.99) |
|
|
<2 | 1 |
|
|
.20 | ||
|
Present | 2.43 (0.62-9.50) |
|
|
Not present | 1 |
|
aCAT: Chronic Obstructive Pulmonary Disease Assessment Test.
bmMRC: modified Medical Research Council.
On average, CAT scores were worse at the end of the study period (6 months). The median (P25-75) change in CAT score was 1 (−3-4).
Change in health status between inclusion and 6-month visits according to the presence and type of exacerbation during the study as recorded in the app.
Change in health status | Stable diseasea | Unreportedb | Reportedc | Mixedd | |
Subjects, n (%) | 6 (9) | 19 (28) | 18 (26) | 26 (38) | N/Ae |
Change in CATf score, median (P25-75) | −3 (−3.5-3) | 1 (−2.2-6.2) | −2 (−7-1.5) | 3 (0-5.2) | <.001 |
Patients with change in CAT score ≥2, n (%) | 1 (20) | 8 (44) | 4 (23) | 19 (73) | <.001 |
aNo exacerbation between inclusion and 6-month visits.
bOnly unreported exacerbation(s) between inclusion and 6-month visits.
cOnly reported exacerbation(s) between inclusion and 6-month visits.
dAt least one unreported exacerbation and one reported exacerbation between inclusion and 6-month visits.
eNot applicable.
fCAT: Chronic Obstructive Pulmonary Disease Assessment Test.
This study provides information about the long-term, consistent use of an mHealth app, Prevexair, to record daily symptoms and detect exacerbations in high-risk patients with COPD, as well as to determine the characteristics of the detected exacerbations and the determinants of reporting them.
The mHealth app market is booming and will continue to grow substantially over the next few years. The growing availability of health apps and the increasing number of patients using smartphones and tablets will encourage health care professionals to incorporate apps into their management plans for patients with chronic disease. This is a step toward ubiquitous health care, thereby allowing patients with chronic disease to self-manage their condition by providing them support to monitor and interpret their own data using mobile devices.
COPD is a highly prevalent disease, occurring in 10% of the population between the ages of 40 and 80 years [
Research has shown that effective management of COPD through integrated care systems, mHealth apps, and other technology has the potential to both benefit the patient and reduce exacerbation costs in the long-term management of the disease [
The results of our study show a high rate of daily use of the app, Prevexair, although there was no contact between the research team and the patient after initiation and no strategy was implemented to continue using the app.
In COPD, little research has been done on diary-keeping, even though diaries have been widely used in studies and clinical trials. In an open, observational study, only 41% of participants achieved 80% compliance using paper diaries that were collected weekly and entered electronically [
In our study, the level of satisfaction with the functionality of the Prevexair app was high. The patients quickly learned how to use the app during the inclusion visit and regularly entered data to record their symptoms and medicine use over 6 months, although they did not make decisions based on information provided by the app during the study. Decisions to change treatment or go to the hospital were made according to usual practice upon orders from the primary care or respiratory specialist treating the patient. Studies that have evaluated feedback from users regarding the functionality and usability of a mobile phone app show us that simplicity and motivation, not age, seem to be the key factors for accepting and using health apps [
With regard to the factors related to continued daily use of the app, in our study, we did not find any differences according to age or sex, comorbidity burden, or disease severity. These results are consistent with other studies that showed that compliance rates were similar across the demographic variables of sex and disease severity [
We have provided new data on use and adherence to a mobile phone app for COPD. The compliance is not affected by demographic factors or disease severity, while clinical or physiological characteristics, such as actively smoked, higher BMI, or were diagnosed with depression, do seem to influence diary use. Nevertheless, a limitation to bear in mind is that other factors related to adherence such as health literacy, prior use of apps, and level of school education could not be evaluated as they were not available.
Simplicity and motivation seem to be the key factors for accepting and using mobile phone apps. However, each user has different needs, so it is important to be able to personalize the app to the patient’s preferences. So, in the patients where we identify factors linked to lower adherence, it is important to offer specific messages such as exercise tracking, monitoring of weight, food intake, and help for tobacco cessation. In addition, personalized self-management plans could be updated according to patients’ needs. Other functionality of interest can be email messaging or any type of communication with health care providers.
Regarding the detection of exacerbations by recording symptoms in the app, it is necessary to highlight the high rate of daily exacerbations and that 62% of the events recorded in the app were not reported and most were not treated. This result is similar to results of earlier studies in London [
In our study, although unreported exacerbations tend to be milder (with a lower number of symptoms and shorter duration of exacerbation), these unreported exacerbations have a clinically relevant negative impact on quality of life (CAT questionnaire) and result in a change in self-administered treatment by the patient in a large number of cases. Patients who did not report their exacerbation were more likely to experience worsening of their health status compared with those who reported exacerbations or those with a stable disease. This may suggest that unreported exacerbations may thus represent an unmet health care need. These results are consistent with other studies, which have shown that unreported exacerbations, despite being associated with less symptom worsening than reported exacerbations, have an important medium to long-term impact on patients’ quality of life [
In our study, the characteristics of the exacerbations were the strongest predictors of reporting. Although there was no direct measure of exacerbation severity, the total number of symptoms at onset and duration of the exacerbation were predictors of reporting. The symptoms associated with reporting an exacerbation in this study were cough and change in sputum color. Sputum color was identified as one of the key determinants of health care utilization in a study looking at patient perspectives on exacerbations [
The identification and correct assessment of COPD exacerbations is important to assess clinical risk and disease control, a goal that is key especially in patients who appear more susceptible to developing exacerbations and are termed frequent exacerbators, similar to our study population, in which monitoring through the app, Prevexair, can be more beneficial.
The app was developed for better lifestyle management for patients with COPD and also to improve monitoring and follow-up by their physicians. In the future, we would like to analyze the usefulness of the app, Prevexair, for physicians during clinician visits for identification of COPD exacerbations and for the correct assessment of clinical risk of COPD, as the app offers the possibility to regularly record relevant health data of a patient’s condition and symptoms. A strategy that could prove useful as several studies suggest that close to half of all exacerbations remain unreported. The unreported exacerbations and consequent lack of treatment by a health care professional were associated with worsening quality of life and increased risk of hospitalization.
A limitation that must be considered in the interpretation of the results is that it provides us information about how app users will perform within the context of a research study as the benefit perceived by the patient is a determining factor in the motivation to use the app. During the study, no decisions were made based on the information recorded in the app. Participants were informed that if they felt ill, they should contact their regular physicians for advice. Other limitations of this study are that we have not evaluated other factors that seem to influence daily compliance and affect both the health status and access to health care, such as socioeconomic status, impact on activities of daily living, and education level. However, in the analysis of factors associated with reporting, we must keep in mind that access to health care is likely to be an independent risk factor for underreporting in the general population. Another potential limitation is that the responses were dichotomous; there were substantial floor and ceiling effects resulting in failure to identify some of the exacerbations because once a symptom is present, no further change will be recorded.
Another limitation is that the study examines a relatively small prospective group of 116 patients monitored for only 6 months. However, as the population was enriched with patients with frequent exacerbators, 262 exacerbations were analyzed, which were equal to a rate of 2.25 per person every 6 months. This event rate can be explained because the relatively high proportion of patients included immediately after hospitalization might have contributed to a higher exacerbation rate and a possible seasonal effect. Missing data in the daily diary were also related to interest. The combination of both missing data and ceiling effects could have resulted in failure to identify some of the reported exacerbations in the daily diary. Furthermore, the analysis used ignored possible differences in symptom trajectory (early recovery from some symptoms) and these differences might be related to reporting.
This study evaluates compliance in the daily use of an mHealth app among patients with COPD having a documented history of exacerbations who are motivated in their self-care for long-term monitoring without a mentoring process or regular phone calls. The findings of this cohort study confirm that daily use of the Prevexair app is feasible and acceptable for reporting daily symptoms and medicine use among people with COPD who are motivated in their self-care because of frequent decompensations of their disease. In addition, this study shows that monitoring through the Prevexair app has the potential for implementation of self-care plans and offers opportunities for interventions in the treatment of patients at risk of frequent exacerbations, identifying symptoms and providing a better diagnosis of their chronic condition. Further research must be carried out to evaluate this strategy for the management of COPD in clinical practice. In the near future, mHealth apps will be a natural complement to health telematics and personal health records. They should be a part of a complete solution to address changes in health care provision, and they are particularly suitable for chronic disease prevention and management.
acute exacerbations of COPD
Chronic Obstructive Pulmonary Disease Assessment Test
chronic obstructive pulmonary disease
forced expiratory volume in one second
GlaxoSmithKline
mobile health
modified Medical Research Council
The authors thank the investigators, Francisco Javier Agustín Martínez, Pulmonology Department, Complejo Hospitalario U de Albacete, and Walther Ivan Giron Matute, Pulmonology Department, Hospital U Gregorio Marañón Madrid, Spain, who participated in the Prevexair study. The authors also thank Astra Zeneca for its financial support to carry out the study. This study has been promoted and sponsored by the Spanish Society of Pneumology and Thoracic Surgery. The financers had no role in study design, data collection, analysis, and decision to publish or in the preparation of this manuscript. This does not alter our adherence to the policies of the
JH, MCR, AG, LM, CD, FG, RR, and JS have intellectually contributed to this work and contributed to data analysis and interpretation of results. JH and MR wrote the manuscript. MF carried out the statistical analysis. All authors participated in drafting and revising the paper and assume accountability for all aspects of the work.
JH has received speaking fees from Boehringer Ingelheim and Gebro Pharma. This does not have a real or perceived conflict of interest between all these sources and this paper. MCR has received speaking fees from Boehringer Ingelheim, AstraZeneca, GlaxoSmithKline (GSK), Menarini, and Novartis and consulting fees from GSK, Gebro Pharma, and Novartis. This does not have a real or perceived conflict of interest between all these sources and this paper. LM has received travel coverings, funds for educative activities, research grants, paid advisories, and participated as Principal Investigator in randomized controlled trials sponsored by private companies or observational studies from Air Liquide, Astra Zeneca, Boston Scientific, Boeringher Ingelheim, Chiesi, ESTEVE, GSK Menarini, MSD, Novartis, Sanofi, Spanish Scientific Societies, and Government and the European Regional Cooperation Fund. This does not have a real or perceived conflict of interest between all these sources and this paper.
CD has received speaking fees from Menarini, GSK, Novartis, Chiesi, Teva, and Ferrer. This does not have a real or perceived conflict of interest between all these sources and this paper. FG has received speaking fees from GSK, Chiesi, Boehringer Ingelheim, Mundipharma, Menarini, Pfizer, Novartis, Esteve, Teva Pharmaceutical, Ferrer, Rovi, Astra Zeneca, Bial, and Actelion y Gebro Pharm. This does not have a real or perceived conflict of interest between all these sources and this paper.