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Going mobile with primary care: smartphone-telemedicine for asthma management in young urban adults (TEAMS)

, PhD, NP-C, , MS, , PhD, , MD, MPH, , MD, , PharmD, MS-MTM, BCPS, BCACP, , MD, MS, , PhD, , MD, , MD, MPH, , & , PhD, MPH show all
Received 17 Jul 2020
Accepted 26 Sep 2020
Published online: 16 Oct 2020



The majority of adults with persistent asthma have chronically uncontrolled disease and interventions to improve outcomes are needed. We evaluated the efficacy, feasibility, and acceptability of a multi-component smartphone-telemedicine program (TEAMS) to deliver asthma care remotely, support provider adherence to asthma management guidelines, and improve patient outcomes.


TEAMS utilized: (1) remote symptom monitoring, (2) nurse-led smartphone-telemedicine with self-management training for patients, and (3) Electronic medical record-based clinical decision support software. Adults aged 18-44 (N鈥=鈥33) and primary care providers (N鈥=鈥4) were recruited from a safety-net practice in Upstate New York. Asthma control, quality of life, and FEV1 were measured at 0, 3 and 6鈥塵onths. Acceptability was assessed via survey and end-of-study interviews. Paired t-test and mixed effects modeling were used to evaluate the effect of the intervention on asthma outcomes.


At baseline, 80% of participants had uncontrolled asthma. By 6-months, 80% classified as well-controlled. Improvements in control and quality of life were large (d鈥=鈥1.955, d鈥=鈥1.579). FEV%pred increased 4.2% (d鈥=鈥1.687) with the greatest gain in males, smokers, and lower educational status. Provider adherence to national guidelines increased from 43.3% to 86.7% (CI = 22.11-64.55) and patient adherence to medication increased from 45.58% to 85.29% (CI = 14.79-64.62). Acceptability was 95.7%; In follow up interviews, 29/30 patients and all providers indicated TEAMS worked better than usual care, supported effective self-management, and reduced symptoms over time, which led to greater self-efficacy and motivation to manage asthma.


Based on these findings, we conclude that smartphone telemedicine could substantially improve clinical asthma management, adherence to guidelines, and patient outcomes.


Under-treatment of asthma, non-adherence, and poor outcomes persist across the U.S. and globally (1,2). Individuals' with uncontrolled asthma have increased risk of morbidity and mortality, diminished quality of life, and elevated symptom burden, and ways to improve clinical care and asthma outcomes are needed (3). Fundamental to this objective is promoting multi-level adherence to evidence-based guidelines (4). It is commonly acknowledged that patients ignore symptoms, take medications inconsistently, and have poor self-management (5,6). However, nonadherence extends beyond patients. Healthcare providers (HCP) conduct sub-standard assessments (7), lack familiarity with guidelines (8), and fail to impart essential self-management skills (9). Furthermore, healthcare systems have limited resources to provide care (e.g. appointment availability, specialist care), and may be difficult for some patients to access (9). Cumulatively, this translates to nonadherence at patient, provider, and systems levels and a reinforcing cycle of inadequate asthma management, with the result that most adults with asthma have chronically uncontrolled disease (10,11).

Improving outcomes is undoubtedly challenging in an era of over-burdened health systems. Physical access has been increasingly constrained due to COVID-19 and resources to provide care are limited. In recent months, telemedicine has been extensively adopted as a means to deliver care safely and remotely (12). This is logical, as there is evidence telemedicine reduces barriers to care, is desirable to patients, and is associated with good outcomes (13,14). However, evidence of efficacy derives predominantly from computer- and site-based programs (15), or asynchronous remote monitoring interventions, whereas many telemedicine visits are now being conducted via smartphone video conferencing to patients at home, representing a substantial change in application (16). This shift is due to the fact that smartphones are more ubiquitous than computers, and smartphone-telemedicine (i.e. mobile visits) has tremendous potential to increase both demographic and geographic reach (17). However, research supporting efficacy of this approach for asthma management is scarce (18). Furthermore, while traditional telemedicine has been shown to improve asthma outcomes in younger patients (19鈥21), few interventions have been tested in adults with asthma or in real-world practice contexts (22鈥24). Thus, a sustainable, practice-integrated, smartphone-telemedicine intervention to improve asthma outcomes and multi-level adherence (i.e. patient, HCP, and systems) is greatly needed. We developed a multi-component smartphone/telemedicine program for adults with asthma (Technology Enabled Asthma Management System 鈥 TEAMS). TEAMS capitalizes on low-cost electronic medical record (EMR) and smartphone technology to provide guideline-based clinical decision support (CDS), remote symptom monitoring, self-management support, and convenient asthma follow-up. The purpose of this study was to evaluate efficacy and acceptability of the TEAMS smartphone-telemedicine program when implemented in a real-world clinical practice context. We hypothesized patients would have improved asthma outcomes at three- and six-months as compared to baseline.


Setting and study sample

This study was approved by the University of Rochester Institutional Review Board [NCT03648203] (18,25). Adult patients (n鈥=鈥33) were recruited from a safety-net practice in NY to participate in a single-arm study. Eligibility criteria for patients were: (1) English speaking, (2) with persistent asthma based on EPR-3 criteria (26), (3) having a smartphone, (4) not pregnant and (5) without confounding comorbidities (cardiac, respiratory). Age range was restricted to younger adults (18鈥44鈥墆ears) on the basis smartphone usage (17). A randomized roster of potential participants (n鈥=鈥140) and their primary care providers (N鈥=鈥5) was generated using the EMR. Patients were recruited by a research assistant (JS) via phone call, and providers were recruited by personal email from the primary investigator.


Technology Enabled Asthma Management System (developer JM) is a multi-component program designed to support predominantly remote primary care management of asthma, and is the first reported program of this type. Full details of development have been published elsewhere (18). Briefly, TEAMS incorporates 3 technological components to augment usual care: (1) smartphone asthma symptom monitoring via a patient's personal smartphone and the MyChart patient portal (Epic EMR), (2) smartphone-based telemedicine follow-up and self-management training (SMT) with a nurse via Zoom video-conferencing, and (3) guideline-based clinical decision support (CDS) software in the EMR that calculates asthma severity, control, and recommended step-wise therapy based on Expert Panel Report-3 (EPR3) guidelines (26,27).

For the intervention, patients were asked to log asthma symptoms (non-urgent) daily via their smartphone, answering questions such as, "Did you have any symptoms of asthma in the past 24鈥塰?" and "What was your peak flow today?" Patients were instructed to call the HCP or seek care for any urgent symptoms as per routine care.

A nurse interventionist conducted home telemedicine follow-up with the patient every 2-6鈥墂eeks until the asthma was well-controlled. Once good control was achieved, follow-up occurred every 2-3鈥塵onths. Symptoms recorded by the patient in the EMR were reviewed prior to the visit. Each video visit included assessment of symptoms, lung function (FEV1), and recent medication use. Using data verified by the nurse, the CDS tool then calculated a detailed asthma assessment, including asthma severity, current control, prescribed step-wise level of therapy, guideline-recommended level of step-wise therapy, dose of inhaled corticosteroid (low, medium, high, micrograms), patient adherence to medication, and an appropriate follow-up plan. An auto-generated progress note was posted by the nurse in the EMR and shared with the HCP to support clinical management and e-prescribing. Patients identified as having uncontrolled asthma received electronic medication adjustments prescribed by the HCP. Phone calls to the HCP were used for urgent follow-up only. Office visits were initiated if indicated by the CDS, or requested by the HCP, nurse, or patient. Self-management training (SMT) occurred at each visit using the Let's Talk About Asthma! series for smartphone, which contains 14 single-page modules covering pathophysiology, symptom monitoring, prevention, management, and communication (28,29). Modules were designed to be covered at least once, with repetition of modules until mastery was demonstrated by the patient.

Data collection and measures

Technology set-up and 0-, 3- and 6-months data collection were conducted in participants' homes by a trained research assistant (JS; years 2018鈥2020). Each patient received $250 for data collection over 6-months. Demographics and baseline asthma information were assessed using smartphone surveys and chart review. Asthma severity was assessed by frequency of symptoms, nocturnal awakening, activity limitations, and short-acting beta-agonist (SABA) use, using EPR3 criteria (27).

Primary efficacy outcomes (0, 3, 6鈥塵onths)

Asthma control was measured using the Asthma Control Questionnaire (ACQ) (30), a 7-item Likert-scale ranging from 0-6. Lower scores indicate better asthma control and a score of 1.5 has a positive predictive value of 0.88 for uncontrolled asthma (31). FEV1 was measured at 0, 3, and 6, months, and each telemedicine visit using an individual Microlife peak flow meter (PFM) provided to each patient, which are accurate to within 5% of the reading or 卤 0.1 liters when compared to traditional spirometry (32). Training in maximal expiration was provided along with the PFM at the baseline visit. Quality of life (QoL) was assessed using the Asthma Quality of Life Questionnaire (AQLQ, 32-item Likert-scale, range 1-7, higher scores indicate better QoL) (33).

Secondary efficacy outcomes (baseline, end-of-study)

Healthcare utilization, corticosteroid use, prescriptions/refills, emergency care, and treatment of comorbidities were assessed via chart review. Emergency care utilization was also assessed by self-report to capture out-of-network visits. Adherence to guideline-based therapy (patient and provider) was calculated by the CDS tool during telemedicine visits based on symptoms, medication, and missed doses.

Usability and Acceptability were measured with the Usability Satisfaction and Ease of Use Questionnaire (USE-Q (34)), a 21-item, 7-point Likert scale (range 1-7), with 7 being the most positive possible score. Usability and acceptability were also assessed via 1:1 interview.

Qualitative interviews

Patient participants engaged in audio-recorded 1:1 interviews to explore experiences and perceptions of the TEAMS program. Interviews lasted about 45鈥塵in, used a semi-structured protocol (), and were conducted by a trained older, White female research assistant (JS) known to patient participants from prior data collection visits. HCPs participated in 1:1 end-of-study interviews with the RA (unknown to HCP) to evaluate their experiences with the TEAMS program.

Feasibility and intervention dose

Intervention dose was calculated as the (a) total number of mobile visits, (b) average visit duration (minutes), (c) SMT dose (minutes of training and modules delivered), and (d) smartphone symptom monitoring dose (days monitoring was performed). Scheduling statistics, visit duration, and technical feasibility data were collected using Redcap surveys completed by the RN at each visit. SMT and symptom monitoring dose were measured using the TEAMS CDS tool. Cost of program implementation was calculated based on total visits (kept and no-show visits), time to conduct visits (minutes late, visit time, documentation, and follow up), median nursing salary ($30/hour) (35) and equipment provided to patients, excluding cost of research-related incentives ($250).

Data analysis

Statistical analysis

Distributional characteristics of the data were assessed using descriptive statistics. Missing data were <1%. Bivariate correlations were examined between demographic and outcome variables using SPSS 25. Paired t-tests were used to compare primary and secondary outcomes from baseline to end-of-study, and Cohen's d was used to evaluate effect size. Independent t-tests were used to examine change in ACQ, AQLQ and FEV1%pred by gender, smoking status, comorbid mental illness, and ANOVA was used to compare outcomes by race/ethnicity and educational level. To confirm the t-test results, linear mixed-effects modeling was conducted using R statistical software to evaluate if ACQ, AQLQ, and FEV1%pred changed over time while controlling for demographic- and health-related factors (age, gender, race/ethnicity, income, smoking status, and education).

Qualitative analysis

Transcribed interviews were analyzed using a consensus approach (JM, JD, KT, AP) and Nvivo12 software (36). Traditional content analysis techniques were used to analyze data for patterns of symptoms and self-management responses (37). Descriptive coding was used to explore perceptions and experiences with the TEAMS program (provider and patient), and pattern coding was used to further develop the themes and clarify concepts (38). Structured memos, member checking, and peer-debriefing were used to maximize validity (39).



Of 140 patients, 65 were reached and screened by phone: 38 were eligible, 1 declined (too busy) and 4 were lost to contact after screening. Thirty-three provided informed consent. Three females dropped out after consenting (2 Black, 1 White). One dropped out prior to intervention due to anxiety, one during the intervention due to time burden, and one was unenrolled by the healthcare provider (HCP) due to suicidal ideation. All others received the intervention and completed the full study (n鈥=鈥30). Of the five HCP solicited by email, 4 responded, consented, and participated (80%). Sample characteristics are shown in Tables 1 and 2. Patient participants (N鈥=鈥30) were predominantly minority (80%), employed, single, and lower socioeconomic status.

Table 1. Patient demographics and baseline asthma characteristics.

Table 2. Provider characteristics.


Most patients had uncontrolled asthma at baseline (80%, defined as ACQ > 1.50). At 3-months, 70% had well-controlled asthma, with 80% being well-controlled at 6-months. Effect sizes for improvements in asthma control and QoL were large (Table 3) and double to triple the minimum clinically important difference. Mean FEV1%pred increased 4.20%. Improvements in FEV1%pred were greatest for smokers (+10.27% vs. nonsmokers +0.68%, CI = 1.72-17.45), males (+11.27% vs. females +0.11%, CI = 3.62-18.72), and those with high-school education or less (+7.94% vs. any college education 鈭0.071%, CI = 0.20-15.82). Patients with worse asthma control benefited most, with a strong correlation between baseline ACQ and improvement in symptoms by end-of-study (r=-0.82, p鈥<鈥0.001). There were no other significant differences in intervention effects based on gender, smoking status, education, race/ethnicity, or presence of comorbid mental illness. Improvement in QoL was strongly associated with improved control (r鈥=鈥0.80, p鈥<鈥0.001) but not with FEV1%pred (r鈥=鈥0.087, p鈥=鈥0.648). The linear-effects models broadly confirmed the t-test results for the outcomes: with statistical significance, ACQ decreased over time, and both AQLQ and FEV1%pred increased over time, indicating better asthma control, quality of life, and pulmonary function.

Table 3. Primary and secondary patient outcomes for TEAMS intervention.

Table 4. Feasibility and intervention dose.

Table 5. Key themes and quotes from end of study interviews with Healthcare Providers (N鈥=鈥4) exploring perceptions of the TEAMS smartphone-telemedicine program for adults with asthma.

Adherence to guideline-based therapy, Table 3

HCP adherence to EPR3 guidelines increased significantly (+1.367 steps, SD = 1.377), with 86.7% of prescribed therapy matching the EPR3-recommendations by end-of-study vs. 43.3% at baseline (CI = 22.11鈥64.55). Patient adherence to control medication increased from 45.58% to 85.29% (CI = 14.79鈥64.62). Preventive visits for asthma and treatment of related comorbidities increased significantly in the year following start of intervention as compared to the year prior. Emergency care utilization decreased marginally, however, use of oral corticosteroids remained unchanged.

Feasibility and intervention dose, Table 4

Nearly 38% of visits occurred after-hours or on weekends, with 93.2% of telemedicine visits having no disruptive technical issues. On average, patients received five 30-min visits over 6-months. Improvements in ACQ, AQLQ and FEV1%pred were not significantly associated with intervention dose, excluding a marginal association between FEV1%pred and the number of days home symptom monitoring was performed (r鈥=鈥0.339, p鈥=鈥0.06). The most commonly repeated SMT topics were knowing if asthma was controlled, understanding different medications, demonstrating correct inhaler technique, and using a PFM. Based on the average nursing time per visit (44.47鈥塵in including visit, documentation and follow up), total visits (148 kept, 47 no-show), median nursing salary ($30/hour) (35) and equipment provided to patients (PFM鈥+鈥塻pacer=$42), the cost of delivering the intervention was estimated at $186.52 per person over 6-months.

Acceptability (HCP), Table 5

HCPs expressed strong satisfaction with the intervention, indicating it saved time and improved workflow, patient/provider knowledge, communication, and adherence to medication. HCPs also noted that their patients increased engagement in care for asthma and other chronic conditions, a finding that was supported in patients鈥 interviews.

Acceptability (patient)

Usability and acceptability were high (mean score = 6.61; SD = 0.47). In interviews, many reported the intervention changed their life, enabling them to take control of their asthma and be active "like a regular person" for the first time as adults.

Overview of qualitative themes (patient)

Nine themes were seen across patient interviews. Nearly all (29/30) reported that: (1) TEAMS worked better for managing asthma than usual care, and (2) greater physical, social and emotional support to manage asthma along with (3) internalized self-management knowledge led to (4) changes in self-management behaviors, which (5) dramatically reduced symptoms over time. When participants saw that self-management changes alleviated symptoms, then they experienced (6) greater self-efficacy; (7) changed beliefs about asthma; and had (8) greater motivation to manage asthma. However, many indicated that (9) ongoing challenges remain. Delineation of these themes and supporting coding schema are presented in Figure 1 with illustrative quotes of individual experiences in Table 6. Key points are described below.

Figure 1. Thematic map of key findings from exit interviews with patient participants regarding the impact of the TEAMS smartphone telemedicine.

Table 6. Quotes from Patient exit interviews (N鈥=鈥30) exploring personal experiences and impact of the TEAMS smartphone-telemedicine program for adults with asthma.

Theme 1: This works. Participants indicated TEAMS was "better than the doctor" because it was more comfortable (i.e. visits could be done from home with less stress), convenient (flexible, no lost work or travel time), effective, and personal. In particular, participants reported feeling connected to the nurse because of being "face-to-face" via video conferencing, whereas in office visits a provider often faces a computer. For example:

P13: Being face to face, there鈥檚 more of a connection. You feel like the person鈥檚 listening - they鈥檙e actually looking at you.

Theme 2: Greater support鈥"Someone cares." Having a nurse who transparently cared, initiated regular asthma follow up, was easily accessible, and assisted with medication management, changed participants willingness to engage in preventive care. This is evident in the following quote:

P9: There was no space for me giving me up鈥擨 had help and support right there. 鈥 Having someone [care]鈥攖hat helped me to care about me.

Being in the program not only made participants feel supported by the nurse, but also enabled them to self-advocate for their asthma needs and develop supportive relationships with family, friends, and HCPs. As one man explained, "It helps me to get the support I need not just from my primary care physician, but people around me." (P17). For many, support included facilitating access to effective controller medication (i.e. medication/dose adjustments), which often required multiple attempts:

P24: We found [medication] that worked. You didn鈥檛 just say, 鈥渙kay, just give it more time.鈥 The symptoms weren鈥檛 [controlled] and you stayed on it and made me feel like my health mattered. 鈥

Theme 3: Internalized knowledge from self-management training (SMT).

Many participants indicated the biggest "eye opener" was realizing uncontrolled asthma causes remodeling (i.e. "scars your lungs"). As one woman remarked, "People don鈥檛 know about scarred tissue in their lungs if they don鈥檛 take the preventive [medication], or what the long-term effect is. They just think it's shortness of breath, like I did." (P10). Others commented similarly, indicating that this "scary" information caused them to be more aware and less accepting of symptoms.

P4: "I realized uncontrolled my asthma was scarring my lungs, and that鈥檚 not good鈥攖hings that I didn鈥檛 pay attention to [before], now I'm mindful of those symptoms."

Other key capabilities included being able to differentiate asthma symptoms (vs. cold), understanding differences between control vs. rescue medication, using a spacer, knowing when an inhaler is running out, and being able to manage an asthma attack. Many reported the digital PFM was essential to recognizing symptoms, providing reassurance and objective evidence of progress:

P5: It鈥檚 hard for someone who has asthma for so long that isn't controlled. You get used to feeling a certain way [and] it feels normal. The meter lets you know your airflow is [low]鈥 didn鈥檛 know that it was that bad鈥 Now, I'm more aware.

Theme 4: Changed self-management. As a result of increased support and SMT, participants began implementing behavior changes. In general, heightened awareness of symptoms corresponded with more proactive self-management approach:

P17: This helped me to monitor and recognize my symptoms鈥 I鈥檝e learned a different response. I know what to do. [Now] I'm taking my preventative 鈥 don鈥檛 tough it out anymore.

For many, establishing an individualized asthma management routine was key to supporting medication adherence. As one woman explained, "I was not good on taking my meds before 鈥ow I have an alarm [and] I鈥檒l take my meds because I got it on the clock" (P12). Routines included keeping control inhalers in one or more high-visibility, easy-access locations, and setting medication reminders on the smartphone. For example:

P13: I used to put [my inhalers] away and forget about it, out of sight out of mind. Now I have medication regiments. If it鈥檚 in front of my face, I remember to do it.

P1: I have one [inhaler] at my toothbrush and one next to the bed at night. It helps me stay on top of my medicine.

Theme 5: Reduced symptoms. As a result of these changes, participants indicated they experienced a dramatic reduction in symptoms, better quality of life, increased ability to be physically active, and reduced anxiety/depression. For example:

P30: Before, I was totally drained, walking around like an 80-year-old. I couldn't do anything because of asthma. [Now] I can breathe. I feel wonderful! I can do things with my kids, like running around.

P25: I felt better and it helped me calm down and relax.

Theme 6, 7, and 8: Changed beliefs about asthma, greater self-efficacy, and greater motivation to manage asthma. Experiencing the benefit of effective self-management was key to changing beliefs about asthma. Many adults indicated they previously refrained from taking medications due to misconceptions, fear of dependence, and beliefs that having symptoms was "normal," but that these belief barriers changed when they saw the medication worked (i.e. seeing benefits is believing medication will work). For example:

P13: I used to think maybe I shouldn鈥檛 take [medication] so often 鈥榗ause I鈥檒l get dependent on it, but now that my symptoms have started to clear, I don鈥檛 feel that way. I feel like it鈥檚 something that prevents it.

P31: I actually see a difference. Before I didn鈥檛, so what鈥檚 the point? Why bother if it鈥檚 not doing anything? Now I can tell鈥攕o it鈥檚 worth doing.

"Seeing is believing" also impacted approach to and perspectives of medical care:

P9: Since doing this [my asthma] has progressed [gotten better], and I鈥檝e actually been going to my appointments, [Before] I would refuse going to the doctor, like, 鈥淥h, they鈥檙e just going to tell me this is not right, that鈥檚 not right鈥︹ But when I seen I was actually making progress, I have more confidence going to the doctor.

It is particularly important to note that it was only after implementing behavior changes and observing a reduction in symptoms that participants developed greater confidence in medical treatment and their personal ability to manage asthma:

P24: [They] found a plan and showed me it can work, and helped me get into a schedule of seeing it work and actually putting it into use, and checking up and following up with me. I have a treatment plan in place, and I'm confident with my treatment of asthma.

This translated to increased motivation to sustain new behaviors, which were now perceived as effective and beneficial:

P14: Before this, I would go to the doctor and they told me to do it, but I didn鈥檛 ever have nobody to push me to doing it. Once I did start taking it and I noticed it was working, I kept doing it.

Lastly, there was an unexpected trickle-down effect of increased adherence to non-asthma medications, and a more proactive approach to managing other chronic conditions:

P17: With me being more on my asthma medication, I鈥檝e been taking all my other medications too, so it鈥檚 helped me address those issues as well.

Theme 9: Ongoing challenges. While participants experienced improvements in symptoms and quality-of-life, ongoing challenges were still present. As seen in Figure 1, these included trouble accessing medications, being busy and forgetting to take care of oneself, and trouble following up with the telemedicine nurse due to personal or technology issues. Most participants felt they would have participated in the program regardless of incentives, however others indicated that incentivizing was essential to their initial willingness to participate:

P20: a lot of people probably wouldn鈥檛 do it [without] incentive. When I first heard about it, I was like "oh yeah, I get paid taking my medicine!" Now that I know how helpful it was, I will [keep taking it].

Despite challenges, most patients indicated that for the first time they felt empowered and in control of their asthma. As one woman expressed so powerfully:

P33: Now, I know what to do. I have asthma, but it doesn鈥檛 have me. It doesn鈥檛 control my life anymore. I can kick asthma鈥檚 butt, but asthma can鈥檛 kick mine. I feel so good about myself鈥


Most young-adults with persistent asthma have poor asthma control under current approaches to care. Our data suggest smartphone-telemedicine could extend healthcare reach and improve outcomes with minimal cost, little workflow disruption, and high patient and provider satisfaction. These findings are timely given the increased use of telemedicine in response to the COVID-19 pandemic, and the absence of best-practice guidelines for delivering asthma care remotely. This manuscript provides details on components of high-quality virtual-care, and compelling preliminary evidence of efficacy, feasibility, and acceptability.

This study utilized a small sample of lower socio-economic status, urban young-adult patients from a single hospital-based clinic, and one nurse interventionist. Furthermore, remuneration for patient participation may have increased participation and retention, resulting in a positive outcome bias. Therefore, broader generalizability may be limited. Nonetheless, it is important to note that most of these "hard-to-treat" patients had achieved good control by end-of-study. This could be attributable to several factors, including increased follow up (40), HCP adherence to stepwise therapy (4), patient adherence to control medication (41), and training in inhaler technique (42), which cumulatively resulted in a higher dose of controller medication being consumed by patients. A randomized trial with more diverse participants is indicated to evaluate replicability, generalizability, and sustainability.

Beyond efficacy and acceptability, other findings emerged that are worth noting. In particular, self-efficacy is often considered a necessary precursor to behavior change (8,43,44). In this study, it appeared that behavior change occurred prior to increasing self-efficacy, suggesting a cyclical rather than linear relationship between concepts (45). In other words, patients who implemented new behaviors and saw evidence of effectiveness developed self-efficacy to continue behaviors, which is consistent with the common-sense model for illness self-management (46). The clinical significance is worth considering: changing asthma management might require a "trial" of new behaviors to demonstrate benefits, develop confidence, and increase willingness to sustain change. Simply put, patients might try it for a period, test it to see if it works, and take it if there is clear observable benefit.

Unfortunately, healthcare providers often ask patients to engage in behaviors they have not observed the value of, as was evident in our baseline interviews. Thus, effort may be needed to overcome apathy and reluctance to change, particularly among those diagnosed with asthma for an extended time. A simple application of the "try it鈥攖est it鈥攖ake it" principle would be to treat aggressively early on. This might include beginning with higher initial dose of controller medication to achieve rapid relief of symptoms, and deescalating to the lowest dose needed to maintain control, as opposed to the usual practice of increasing dose when symptom relief is not achieved after weeks or months.

Second, patients might need more follow-up and SMT than is common in practice (47). At present, most SMT is geared toward children/adolescents, caregivers, or high-risk older-adults (48). However, evidence indicates young-adults have poor understanding of asthma management and may recall little of asthma education as children (49). Thus, retraining for these patients may be warranted. Our participants were encouraged to follow up every 2鈥墂eeks until good control was achieved, amounting to 5 visits and 80鈥塵in of personalized SMT, per patient. Based on this, we conclude it is unlikely adults can be supported to achieve good control under current care models. Improving outcomes will require a system-level commitment to aggressive follow-up, training, and support to overcome the many barriers to change.

Conclusion . Good asthma control may be achievable using revolutionary care models (i.e. smartphone telemedicine with clinical decision support) as an extension of usual care. This approach can enable quality care for patients with asthma in a way that does not increase risk of exposure to infectious diseases, such as COVID-19.

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The authors wish to thank the patients and providers who participated in this study.

Declaration of interest

No potential competing interest was reported by the authors.


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