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. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: Public Health Nurs. 2021 Oct 11;39(2):405–414. doi: 10.1111/phn.12986

Promoting risk reduction among young adults with asthma during wildfire smoke: A feasibility study

Julie Marie Postma 1, Tamara Odom-Maryon 2, Ana G Rappold 3, Hans Haverkamp 4, Solmaz Amiri 5, Ross Bindler 6, Justin Whicker 7, Von Walden 8
PMCID: PMC8930445  NIHMSID: NIHMS1748857  PMID: 34636066

Abstract

Objective(s):

This study explored the feasibility, acceptability, preliminary impact, and functionality of two risk reduction mobile application (app) interventions on asthma outcomes as compared to a control arm during wildfire season.

Design:

Three-arm, 8-week randomized clinical trial.

Sample:

Sixty-seven young adults with asthma were enrolled.

Measurements:

The Asthma Control Test, forced expiratory volume in one second (FEV1) and the System Usability Scale were measured at baseline, 4, and 8 weeks. The Research Attitude Scale was administered at 8 weeks. Twenty participants from the two intervention arms completed an optional survey and six were interviewed after completing the study.

Intervention:

Both intervention arms could access Smoke Sense Urbanova, an app that supports reducing risks from breathing wildfire smoke. The Smoke Sense Urbanova Plus arm also monitored their daily FEV1, received air quality notifications, and accessed preventive tips and a message board.

Results:

Most participants agreed the app and spirometer were usable and their privacy and confidentiality were maintained. No adverse events were reported.

Conclusions:

Participant-identified recommendations will support intervention refinement and testing. This research supports asthma self-management tools that public health nurses and community health workers can recommend for at-risk populations.

Keywords: asthma, clinical trial, mobile applications, risk reduction behavior, wildfires, young adult

1 |. BACKGROUND

Climate change, land management practices, and population growth into wildland-urban interfaces are lengthening and intensifying the wildfire season, making intervention vital (Abatzoglou & Williams, 2016). Worldwide, an estimated 339,000 deaths annually are attributable to wildfire smoke exposure (Johnston et al., 2012). Exposure to unprecedented levels of wildfire smoke is increasing cardiopulmonary mortality and is detrimental in people with asthma (Reid et al., 2016). In the U.S., one in eight young adults has asthma (CDC, 2017). Public health recommendations to minimize exposure to poor air quality (AQ) range from wearing a respirator to staying indoors. Young adults are less likely to adhere to AQ alerts than older adults (Dantoni et al., 2017), yet asthma management practices are forming during young adulthood.

Little evidence exists on the effectiveness of AQ alerts on objective measures of behavior and health (Dantoni et al., 2017; Fish et al., 2017). Smartphone applications (apps) show promise as a risk communication strategy, but objective outcomes are infrequently tested (Khusial et al., 2020). The app, breathe, increased asthma quality of life in the intervention arm, but no clinical outcomes were measured (Licskai et al., 2016). The AirRater app was “highly useful” and supported decision-making about daily activities (Campbell et al., 2020), however the study design precluded an assessment of effectiveness. The app myAirCoach demonstrated effectiveness in improving asthma control but not lung function at 6-months (Khusial et al., 2020). The U.S. Environmental Protection Agency’s (EPA) Smoke Sense app has been widely used, but self-selected users responded to symptoms rather than prevented symptoms via risk reduction (Rappold et al., 2019). Rappold et al. (2019) recommend that messaging be personally relevant and reinforce benefits of healthy behaviors; however, the app’s impact on health outcomes has not been tested in a randomized trial design.

Few studies have measured the effectiveness of apps on objective measures of wildfire smoke risk reduction or asthma-related clinical outcomes. Further research is critical to fill this evidence gap and rigorously test if interventions minimize exacerbations and lung damage and through what mechanisms (Hano et al., 2020). The purpose of this study was to explore the feasibility (recruitment, enrollment, and retention), acceptability (intervention engagement, fidelity, usability, and research attitude), functionality and preliminary impact of two risk reduction mobile application interventions that incorporate Smoke Sense on asthma control and lung function compared to a control arm during wildfire season. Participants identified recommendations to support intervention refinement and testing.

2 |. METHODS

2.1 |. Design

A three-arm, unblinded, randomized controlled trial was employed. Outcome data were collected at baseline, 4, and 8 weeks. Frequency of app use, per feature, was collected throughout the study period.

The study included one control arm and two intervention arms (Figure 1). Intervention arms were Smoke Sense Urbanova (SSU) and SSU-Plus to differentiate our study from the ongoing EPA Smoke Sense Study Citizen Science project (EPA, n.d.).

FIGURE 1.

FIGURE 1

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Mobile application features per study group

Participants in the intervention groups created a profile and recorded their wildfire smoke observations and health symptoms and were encouraged to explore the reports of other users. In the 1) Symptoms & Smoke Observations tab, participants reported weekly observations of smoke, symptoms, and exposure reduction behaviors; engagement occurred by viewing cumulative statistics of other users’ reports. In the 2) Fire & Smoke Near Me tab, participants received current air quality (AQ) and next day forecast. The 3) AQ 101 and 4) SmokeSmarts modules tested knowledge of AQ facts. Virtual badges were awarded to promote desired behaviors by level of engagement (User, Explorer, Observer, Learner, and Smarty Pants).

SSU-Plus had additional social features to maximize risk reduction. Participants were asked to engage in SSU, plus: 5) Use a spirometer twice daily to monitor FEV1; 6) Use the “Map It” feature by providing a spirometry reading upon receipt of a poor AQ notification illustrating changes in FEV1 with color-coded lung icons (red, yellow, green) on a shared (SSU-Plus participants- posted de-identified results) regional map; 7) Review the weekly, preventive Tips and; 8) Post a response to the tips into the app’s shared message board.

Study data were collected directly using Research Electronic Data Capture (REDCap) and a cloud server linked to an app the team created (U-TRAK). App functions were enabled to align with arm assignment. App data collection included Global Positioning System (GPS) coordinates, app usage, and forced expiratory volume in one second (FEV1) as measured via the Spirobank Smart spirometer (Medical International Research [MIR], n.d.). Our technology partner, Urbanova (https://urbanova.org), integrated software they developed (e.g., message board), purchased (e.g., spirometry software development kit, GPS) or replicated (e.g., EPA’s Smoke Sense) to host U-TRAK.

Enrolled participants received a spirometer and a 45-min videoconference orientation with the research coordinator. Spirometry training was provided (Graham et al., 2019). Participants were briefed on the study schedule and received emails with links to REDCap surveys for completion within 24-h. Participants in both intervention arms received an overview of Smoke Sense features and were instructed to use them weekly.

Participants received an electronic gift card at each time point if they completed data collection. All participants kept their spirometers ($100 value) and were given access to the spirometer software and the EPA’s Smoke Sense app at study completion. Study activities were reviewed and approved by the university’s institutional review board.

2.2 |. Sample and randomization

Eligible participants were young adults, 18–26 years of age, who self-reported having asthma diagnosed by a health care provider. They were required to own a smart phone (Android or iOS platforms) and understand English. Exclusion criteria included surgery within 3 weeks of enrollment or a cardiovascular condition (contraindications for spirometry). Participants were ineligible if they reported being current or former tobacco smokers, had COVID-19 prior to/at the time of screening or had previously used the EPA’s Smoke Sense app. Participants were recruited from university student listservs in the Western U.S. Eligibility screening was conducted by the research coordinator via a REDCap survey or phone call. If eligible, informed consent was obtained electronically.

Participants were randomized with equal probability to one of three study arms in blocks (3,6 or 9) using a random number generator. After consent was obtained, the research coordinator accessed the master assignment list to determine the next available assignment.

2.3 |. Measures

2.3.1 |. Feasibility and acceptability outcomes

Study feasibility was assessed by examining recruitment and retention. Intervention fidelity was assessed through U-TRAK’s user action log which recorded the number of badges earned when using Smoke Sense and message board use (SSU-Plus only). Badges reflect five levels of engagement that align with weekly use of app features: User, Explorer, Observer, Learner, and Smarty Pants.

Acceptability of the portable devices and app features was measured at each study timepoint using the System Usability Scale (SUS) (Brooke & McClelland, 1996). The Research Attitude Scale was used to measure participants’ level of agreement with statements about research at week 8 (Rubright et al., 2011).

2.3.2 |. Clinical outcomes

The Asthma Control Test (ACT) measures 5 items (shortness of breath frequency, asthma symptoms, use of rescue medications, daily functioning, and asthma control) via a 4-week, self-report recall. Scores range from 5 (poor control) to 25 (complete control) (Nathan et al., 2004). A score > 19 indicates well-controlled asthma (Schatz et al., 2006). It is reliable (Test-retest 0.77; Internal consistency Cronbach’s α = 0.84-0.85 [cross-sectionally]; 0.79 [longitudinally]), valid, and correlates with specialists’ ratings of asthma control based on history, examination and FEV1 (Schatz et al., 2006).

Forced expiratory volume in one second is the maximum amount of air that the subject can forcibly expel during the first-second following maximal inhalation and is regularly monitored in patients with asthma (Graham et al., 2019). Spirometry is used to objectively measure FEV1. Decreases are indicative of airway narrowing. The portable MIR Spirobank spirometer has been validated against office spirometry (Degryse et al., 2012). The percent of predicted FEV1 was calculated using the Global Lung Function Initiative’s online calculator based on age, height, and sex (Quanjer et al., 2012).

Participants were sent an email at baseline, 4, and 8 weeks that included a survey link to complete the ACT. They were also instructed to measure their FEV1 using the Spirobank Smart spirometer via the U-TRAK application.

2.3.3 |. App functionality outcomes

A sub-sample of intervention participants willing to be re-contacted completed an optional survey and interview after exiting the study to evaluate the app and recommend refinement. Response options included Likert scales and yes/no prompts with open-ended comment fields. Survey respondents were invited to participate in a 15-min interview by telephone. Interviews were conducted in December 2020. All conversations were audio-recorded, and recordings were transcribed.

2.3.4 |. Fine particulate matter

The daily average level of fine particulate matter (PM2.5) during each participant’s exposure period was accessed from www.airnowtech.org. Given that the ACT reflected the past month’s exposure, exposure period was defined as the 28 days preceding baseline through the week 8 measurement. This information allowed quantification of exposure to unhealthy air that aligned with the assessment of asthma control and FEV1.

2.4 |. Sample size

Sample size (n = 60) was based on assessing the feasibility and acceptability of the SSU and SSU-Plus interventions. We hypothesized that 75% of eligible participants would consent to participate (Licskai et al., 2016; Morita et al., 2019) and that 75% would complete the study (Liu et al., 2011). We planned to recruit up to 86 eligible individuals to consent 60 or more of these individuals to participate (89% probability assuming 75% consent based on the cumulative binomial probability). We expected that at least 42 out of 60 participants would complete the study (85% probability if the true rate of completion is 75%) (Liu et al., 2011).

2.5 |. Analysis

Categorical and continuous data were summarized as frequencies (percentages) and means (standard deviations [SDs]) or medians (percentiles) based on normality of the data. Demographic and clinical characteristics of each arm were summarized using descriptive statistics. Feasibility outcomes were estimated and compared to achievement of hypothesized rates. Hypothesis testing was conducted at the 0.05 level (alpha = 0.05).

Acceptability outcomes were examined as follows: 1) Participants could earn 1 badge in each category per week during the 8-week study period. Intervention engagement through app usage was summarized as percent activity completed (total number of each badge earned during study period/8)*100. Mean differences were compared using a one-sided two-sample t-test; 2) For SSU-Plus, twice daily spirometry engagement was expressed relative to the number of days on study. The percent activity completed was calculated as: (number of morning and evening spirometry measurements taken by the participant/twice the number of days on-study)*100. Text generated from interviews were analyzed using thematic analysis (Krueger & Casey, 2009).

PM2.5 exposure was expressed using the AQ Index (AQI) thresholds for PM2.5 (μg/m3) (EPA, 2018). Based on when spirometry measurements and surveys were completed, the number of days in participants’ exposure periods varied. The percent of days with a 24-h average PM2.5 level at or above each of the AQI thresholds for PM2.5 (12.1, 35.5, 55.5, 150.5 and 250.5) during each participant’s exposure period were calculated and then summarized using box plots.

To explore the preliminary impact of the interventions on clinical outcomes, the change in total ACT score and change in percent of predicted FEV1 at week 8 from baseline were calculated. By study arm, the mean change for each outcome was tested using the one-sided, paired t-test. For this comparison, we constrained the sample to those participants who completed the ACT and FEV1 measures: 1) within 3 days of their scheduled time-to-complete based on their on-study date (defined as when participants submitted baseline ACT), and 2) within 24 h of each other. This feasibility study was not designed or powered to support statistical comparisons of study arms using multivariable regression modeling.

3 |. RESULTS

A 3-month recruitment period began on 7/1/2020. The first participant was enrolled on 8/6/2020. All participants completed primary data collection by 12/14/2020. The optional post-study survey and interviews were completed by 12/31/2020.

3.1 |. Feasibility

Eighty-one participants were assessed for eligibility. The percentage of eligible participants who enrolled (n = 67, 83%) was higher than the hypothesized value of 75% (Figure 2). Participants were randomly assigned to one of three arms: Control (n = 22), SSU (n = 22) and SSU-Plus (n = 23). Attrition was minimal. Most participants (n = 64; 96%) completed the week 8 data collection suggesting participation was feasible. Twenty participants, 10 from each intervention arm, completed the optional post-study survey. Of the survey respondents, 6 participants were interviewed, 3 per intervention arm.

FIGURE 2.

FIGURE 2

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Participant flow diagram

3.2 |. Baseline data

Demographic and clinical characteristics are reported by arm in Table 1. Most participants identified as female (79%), non-Hispanic (88%), and white (78%). Most reported using an iphone (77%) versus Android (23%) operating system. At baseline the average ACT score was 20.4 (2.5), and the mean percent predicted FEV1 was 93.1% (17.8). Over half (55%) of participants reported being prescribed an asthma maintenance medication yet half reported ‘less than prescribed’ usage.

TABLE 1.

Demographics and clinical characteristics per Study Arm

Participant Characteristics SSU-Plus
n = 23* 		SSU
n = 22* 		Control
n = 22*
Gender; n (%)
Female 17 (77%) 16 (73%) 18 (86%)
Male 5 (23%) 5 (23%) 3 (14%)
Other 0 (N/A) 1 (4%) 0 (N/A)
Age; mean years (SD) 21.9 (2.05) 21.8 (2.81) 21.9 (2.50)
Ethnicity; n (%)
Hispanic or Latino/a 1 (5%) 5 (23%) 2 (9%)
Non-Hispanic or Non-Latino/a 21 (95%) 17 (77%) 19 (91%)
Race; n (%)
Asian 2 (9%) 0 (N/A) 1 (5%)
Native Hawaiian or Other Pacific Islander 0 (N/A) 1 (5%) 0 (N/A)
White 18 (78%) 17 (77%) 17 (77%)
More than One Race 2 (9%) 3 (13%) 3 (13%)
Unknown or Not Reported 1 (4%) 1 (5%) 1 (5%)
Risk Perception and Self Efficacy; n (%) agreement
Wildfire Smoke is a Common Occurrence Near Me 20 (87%) 12 (71%) 11 (61%)
Wildfire Smoke Can Impact My Health 19 (83%) 16 (94%) 15 (83%)
Alerts Help Me Reduce My Exposure to Wildfire Smoke 21 (91%) 16 (94%) 16 (89%)
Risk Reduction Resources; n (%)
Access to AQI Data 17 (74%) 12 (55%) 13 (59%)
Car with Recirculate Mode for Ventilation System 14 (61%) 13 (59%) 7 (32%)
Air Conditioning – Home 13 (57%) 12 (55%) 8 (36%)
N95 (or similar) Respiratory Mask 10 (44%) 6 (27%) 5 (23%)
Air Conditioning – Work 8 (35%) 6 (27%) 8 (36%)
High Efficiency Particulate Air Filter 6 (26%) 4 (18%) 1 (5%)
Prescription for Maintenance Medication; n (%)
Yes 12 (55%) 12 (55%) 12 (57%)
Prescription Maintenance Medication Use ; n (%)
Less than Prescribed 5 (42%) 7 (58%) 6 (50%)
Exactly as Prescribed 7 (58%) 5 (42%) 5 (42%)
Prescription for Rescue Medication^; n (%)
Yes † 20 (91%) 18 (82%) 18 (86%)
Prescription Rescue Medication Use‡; n (%)
Less than prescribed 12 (60%) 8 (44%) 6 (33%)
Exactly as prescribed 8 (40%) 10 (56%) 10 (56%)
Asthma Control Test; mean score (SD) 20.3 (2.2) 20.7 (1.8) 20.3 (3.4)
FEV1; mean L (SD) 3.40 (1.00) 3.69 (0.64) 3.14 (0.78)
Mean Percent of predicted FEV1 (SD) 93.4 (18.6) 96.2 (14.1) 89.8 (20.1)
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SSU: Smoke Sense Urbanova; n: Number of Participants;%: Percent; SD: Standard Deviation; AQI: Air Quality Index; FEV1: Forced Expiratory Volume in One Second; L: Liters.

*

Due to rounding and participant nonresponses, summing percentages may not equal 100%.

˜

One participant (8%) in the Control arm reporting using their prescription maintenance medication more than prescribed.

^

Two participants (3.1%), one in the Control arm and one in SSU-Plus reported oral steroid use in the month preceding the start of the study.

†

All participants, except one in SSU arm, were prescribed albuterol, and/or an albuterol combination product as rescue medication.

‡

Two participants in the Control arm (11%), indicated they use their rescue medication more than prescribed.

Most participants agreed that: wildfire smoke is a common occurrence where they live (74%); a few hours of wildfire smoke can impact their health (86%); and that information alerts are likely to help reduce exposure to wildfire smoke (92%). However, access to risk reduction resources varied with 63% reporting access to AQI data, 51% reporting a car with a recirculation mode for ventilation, and 49% reporting air conditioning at home. Approximately one third (31%) reported access to an N95 mask. Less than one fifth (16%) reported access to high efficiency particulate air filters.

3.3 |. Acceptability and usage

Intervention participants (n = 45) used Smoke Sense primarily to view the AQI and explore the fire and smoke map (Table 2). Participants observed their symptoms, smoke observations and exposure reduction activities less frequently, and were least engaged in learning risk reduction strategies. Differences by arm were not statistically significant.

TABLE 2.

Application Engagement by Intervention Study Arm

Activity Action Required Percent of Each Activity Completed During Study Period (SD)***
SSU (n = 22) SSU-Plus (n = 23)
User Badge* Launch application weekly to view current AQI and forecasted AQI. 56 (31) 73 (31)
Explorer Badge* Explore the “Fire and Smoke” map weekly. 34 (28) 44 (31)
Observer Badge* Report health “Symptoms, Smoke Observations” and exposure reduction behaviors weekly. 27 (22) 27 (22)
Learner Badge* Complete “Air Quality 101” lesson weekly. 14 (22) 22 (30)
Smarty Pants Badge* Complete “Smoke Smarts” survey weekly. 16 (17) 21 (29)
SSU-Plus Spirometry** Take twice daily spirometry readings n/a 85 (11)
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SSU: Smoke Sense Urbanova; n: Number of Participants; SD: Standard Deviation; AQI: Air Quality Index; SD: standard deviation; n/a: not applicable.

*

For badges, percent activity completed was calculated as (number earned/8) X 100. Participants could earn 1 badge in each category per week during the 8-week study period.

**

For SSU-Plus spirometry, percent activity completed was calculated as (number of morning and evening spirometry measurements taken by the participant/twice the number of days on-study) X 100.

***

Using a two-sample t-test, no differences in the percent of activity for badges were found (all p > .05).

SSU-Plus participants received additional app features. Spirometry compliance was 85% (Interquartile range 77%–95%). Participants opened the “Plus” menu more than once a week. More participants opened and viewed the message board (63%) than posted messages. The message board was used infrequently with 26% (n = 6) of participants posting at least once for a total of 12 posts.

Pursuant to the purpose of this study, revisions to the app were required as the study progressed. As such, findings from the System Usability Scale are specific to the app version the participant was using when they completed the scale at weeks 4 and 8. The initial iOS version of the app was released on 8/9/2020 and the Android version was released 8/12/2020. A significant update was released on 9/4/2020 that included the Message Board and Map It features. Other updates (6 releases on iOS; 4 releases on Android) were made to fix errors that caused the app to malfunction and improve formatting. Mean scores over time on a scale from 0–100 were 81 (12) at 4 weeks (n = 59) and 83 (12) at 8 weeks (n = 62) suggesting a stable and usable system.

The mean score on the Research Attitude Scale was 44 (4), indicating that participants (n = 61) reported a positive attitude about medical research. Most participants (88%) agreed or strongly agreed that their privacy and confidentiality would be maintained. No adverse events were reported.

3.4 |. Intervention functionality

A sub-sample of intervention participants (n = 20; 10 per intervention arm) completed an optional survey and interview (n = 6; 3 per intervention arm) after study completion. All survey respondents reported liking the app and 90% would recommend it to others, no matter their intervention arm.

Most (90%) SSU-Plus respondents agreed that the app helped their asthma management compared to 50% of SSU respondents. Most SSU-Plus respondents agreed that AQ advisories (89%), daily spirometry readings (78%) and the “Map It” feature (67%) helped them manage their asthma. In contrast, only half (50%) and one-third agreed that the “weekly tip” and “message board” helped them manage asthma.

Interviewees reported that if more people had posted, the message board would have been helpful. They recommended weekly reminders to post messages. SSU-Plus participants recommended graphing spirometry results over time. Multiple participants wanted “more information about what the [spirometry] readings actually meant.”

All respondents reported that the onboarding videoconference helped them understand the app, study tasks, and spirometry technique. The majority (95%) reported that the app did not negatively impact their phone’s functionality or data usage, however some (20%) reported a negative impact on battery life.

3.5 |. Exposure to poor air quality

Participants’ exposure periods averaged 78.2 (SD 14.2) days. Ranging from 0 to 17.2%, 8.5% (median) of days (Interquartile Range: 6.0, 11.9) during a participant’s exposure period exceeded the acceptable level for PM2.5 under federal daily standards (35.5 μg/m3) (Figure 3). This level is described as “Unhealthy for Sensitive Groups” or worse according to the AQI PM2.5 thresholds. Over one-third (35.3%) of days were deemed ‘Moderate’.

FIGURE 3.

FIGURE 3

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Percent of days during participants’ exposure period at, or above, daily PM2.5 thresholds

3.6 |. Clinical outcomes

Thirty-seven participants (13 Control, 7 SSU and 17 SSU-Plus) recorded both ACT and FEV1 measures that were within the study visit window (3 days from scheduled) and within 1 day of each other at both baseline and week 8. For the SSU-Plus arm, a small, but statistically significant increase in ACT at week 8 (Mean [SD]: 21.5 [2.3]) compared to baseline (20.0 [2.4]) was observed (p = .0008). A significant decrease in percent predicted FEV1 at week 8 (88.6% [17.2]) compared to baseline (94.9% [16.2]) was observed (p = .0172). For the SSU arm, no difference in ACT at week 8 (21.0 [4.0]) from baseline (21.3 [2.1]) or in percent predicted FEV1 at week 8 (95.6% ([17.2]) compared to baseline (97.6% [14.6]) was observed (all p > .05). For the control arm, a statistically significant increase in ACT at week 8 (22.4 [1.9]) compared to week 0 (20.2 [3.7]) was observed (p = .0320) but no change in percent predicted FEV1 at week 8 (92.9% [16.0]) compared to week 0 (88.4% [20.2]) was observed (p > .05).

4 |. DISCUSSION

The study procedures, use of portable spirometry and smartphone apps were feasible and useable for young adult participants with asthma. Performance exceeded our hypotheses in terms of the proportion of people who consented and completed the study. We successfully recruited through university listservs and used an electronic consent process. We oriented participants via videoconference and collected all data remotely. These adaptations reflect other researchers’ experiences conducting clinical trials during the coronavirus pandemic (McDermott & Newman, 2021).

The observed changes in ACT and percent predicted FEV1 should be interpreted with caution. Examination of changes in asthma outcomes was limited by the design and purpose of this feasibility study. Participants completed the instruments and spirometry measurements; however, we did not build constraints into the app to ensure that these data were collected concurrently. As such, 45% (n = 30) of the participants could not be included in the analysis looking at change in the clinical outcome because their measurements fell outside the study visit window or were not taken within 1 day of each other. Only 7 (32%) of the SSU participants were included in the analysis. Observed changes, or lack thereof, could relate to participants’ mild asthma as indicated by half the sample not reporting a prescription for a daily controller medication, or that their asthma was, on average, well-controlled at baseline. A similar RCT found no significant change in FEV1 and improvement in asthma control (Khusial et al., 2020). However, the intervention period was longer (4 months), and inclusion criteria included treatment with a controller medication.

Findings may be related to intervention adherence and engagement. Adherence to using SSU’s educational features was suboptimal among both intervention arms but use varied by feature. Participants were more engaged in identifying their AQ and response to wildfire smoke. Morita et al. (2019) reported that 67.5% of their participants used their app weekly (as measured by logging in) within the first 4 weeks, with 57.7% using the app in week 45 of their study. Khusial et al (2020) reported that their app was used “often” but actions per feature were not recorded. SSU-Plus participants demonstrated high compliance with spirometry but low engagement using the message board, which was not fully functional at the start of the study. Participants found the app acceptable and offered strategies to improve engagement with the features, including the message board.

Young adulthood is a critical developmental period to support healthy habits, yet few health interventions have demonstrated efficacy in this population (Committee on Improving the Health Safety & Well-Being of Young Adults, 2015). Evidence supports the behavioral impact that air pollution has on people’s physical activity. In a 2018 systematic review and meta-analysis, by An et al., “All studies found air pollution level to be negatively associated with physical activity …” (An et al., 2018, p.115). However, authors note that most studies examine the impact of air pollution on disease development versus its influence on health behaviors. Integrating cognitive and behavioral health theories maximizes the impact of mobile health (mHealth) interventions and will motivate app refinement (Yang & Van Stee, 2019). We hypothesize that enhancing perceived susceptibility and severity by training participants to create healthy habits in consideration of air quality will impact asthma monitoring and control.

The app intervention could be improved by teaching participants how to interpret spirometry results and integrating AQ with FEV1 into a visual that displays that relationship over time. Integration of preventive messages on good air quality days, such as encouraging use of daily preventive medications and checking forecasts for planning activities, may also strengthen the intervention. Finally, reinforcing the benefits of protective behaviors across a broader range of outcomes, such as getting a good night’s sleep versus avoiding an emergency room visit, is recommended for app revision (Hano et al., 2020; Rappold et al., 2019). User recommendations to enhance app function will likely increase engagement so we can better test the app’s impact on asthma outcomes.

4.1 |. Recommendations for future research

All study participants were exposed to several days of unhealthy air during the exposure period. Future research will examine the timing of PM2.5 exposure on FEV1 via an analysis of the exposure-lag-response relationship. Current evidence suggests respiratory effects may lag smoke exposure between 0–5 days (EPA, 2019). Outcome measurements will be timed accordingly in subsequent studies to ensure that self-report data and spirometry are collected concurrently. Adding a daily asthma trigger survey would improve our ability to understand other factors influencing airway function (Ritz et al., 2006), such as seasonal allergies and viruses, and improve our ability to detect changes at a more granular level.

4.2 |. Implications for public health nursing

Multiple populations are vulnerable to air pollution from wildfire smoke. The young adults studied here were a logical choice to test the interventions’ acceptability, given their near universal use of smartphones (Pew Research Center, 2019). We will continue to refine the SSU-Plus app for people with asthma. In the meantime, the EPA Smoke Sense app is free and available now in English and Spanish. Public health, primary care, occupational health, and school nurses are well-positioned to recommend the app to their clients and the public.

4.3 |. Limitations

Due to COVID-19, we recruited a community-based convenience sample versus a representative clinic-based sample. There was limited racial and ethnic diversity in the sample. Oversampling from minority populations and a broader sampling frame would create a more representative sample. Asthma and medication use was self-reported. Findings may not be generalizable.

The number of days “on study” varied given activities were modified to be mobile versus in-person causing some variability in participant submission of data. We were unable to teach participants spirometry technique face-to-face. SSU-Plus features were not fully developed when the app was initially made available to participants, which likely impacted intervention fidelity. Tracking app usage was initiated 4 weeks into the study. Follow up was limited to 8 weeks and baseline data collection reflected an exposure period that, in general, included wildfire smoke.

Exposure misclassification is a study limitation. We intended to collect latitude and longitude continuously via GPS. However, the app required ongoing permission from participants. Some chose not to opt into location sharing throughout the trial. Instead, location was reliably collected when participants opened the app to perform their spirometry. Our AQ data assumed a static location between data collection points. This intermittent collection of location data also prevented us from objectively measuring the recommendation to stay indoors. We did not measure indoor AQ. Future work could integrate indoor and/or wearable AQ sensors to improve exposure accuracy (Mousavi & Wu, 2021).

Lack of blinding may introduce bias. We were unable to blind the research coordinator or the participants to arm status. However, neither the PI or the biostatistician had direct contact with study participants and fidelity assessments were objective and standardized.

5 |. CONCLUSION

This study explored the feasibility, acceptability, preliminary impact, and functionality of two risk reduction mobile application (app) interventions on asthma outcomes as compared to a control arm during wildfire season. Participant-identified recommendations will support intervention refinement and testing. This research supports asthma self-management tools that public health nurses and community health workers can recommend for at-risk populations.

ACKNOWLEDGMENTS

The research team would like to acknowledge the study participants, the undergraduate nursing students who contributed to this research (Hannah O’Flanagan; Alison Clinton; April Easter) and Kim Zentz at Urbanova for her leadership and collaborative spirit. Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R21NR019071. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding information

National Institute of Nursing Research, Grant/Award Number: R21NR019071

Footnotes

Publisher's Disclaimer: EPA disclaimer: The views expressed in this manuscript are those of the individual authors and do not necessarily reflect the views and policies of the U.S. Environmental Protection Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

REGISTRATION AND TRIAL PROTOCOL

This study has been registered on ClinicalTrials.gov: NCT04724733.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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