JMIR FORMATIVE RESEARCH

McGinnis et al

Original Paper

A Digital Therapeutic Intervention Delivering Biofeedback for Panic
Attacks (PanicMechanic): Feasibility and Usability Study
Ellen McGinnis1, PhD; Aisling O'Leary2, BSc; Reed Gurchiek2, PhD; William E Copeland1, PhD; Ryan McGinnis2,
PhD
1

Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, United States

2

M-Sense Research Group, University of Vermont, Burlington, VT, United States

Corresponding Author:
Ryan McGinnis, PhD
M-Sense Research Group
University of Vermont
33 Colchester Avenue
Burlington, VT, 05401
United States
Phone: 1 2025095783
Email: ryan.mcginnis@uvm.edu

Abstract
Background: Panic attacks (PAs) are an impairing mental health problem that affects >11% of adults every year. PAs are
episodic, and it is difficult to predict when or where they may occur; thus, they are challenging to study and treat.
Objective: The aim of this study is to present PanicMechanic, a novel mobile health app that captures heart rate–based data
and delivers biofeedback during PAs.
Methods: In our first analysis, we leveraged this tool to capture profiles of real-world PAs in the largest sample to date (148attacks from 50 users). In our second analysis, we present the results from a pilot study to assess the usefulness of PanicMechanic
as a PA intervention (N=18).
Results: The results demonstrate that heart rate fluctuates by about 15 beats per minute during a PA and takes approximately
30 seconds to return to baseline from peak, cycling approximately 4 times during each attack despite the consistently decreasing
anxiety ratings. Thoughts about health were the most common trigger and potential lifestyle contributors include slightly worse
stress, sleep, and eating habits and slightly less exercise and drug or alcohol consumption than typical.
Conclusions: The pilot study revealed that PanicMechanic is largely feasible to use but would be made more so with modifications
to the app and the integration of consumer wearables. Similarly, participants found PanicMechanic useful, with 94% (15/16)
indicating that they would recommend PanicMechanic to others who have PAs. These results highlight the need for future
development and a controlled trial to establish the effectiveness of this digital therapeutic for preventing PAs.
(JMIR Form Res 2022;6(2):e32982) doi: 10.2196/32982
KEYWORDS
mental health; mHealth; biofeedback; panic attack; digital medicine; app; mobile health; application; biofeedback; mobile phone

Introduction
Background
Almost one-third (>28%) of adults have experienced a panic
attack (PA) and >11% have experienced one in the past 12
months [1]. People experiencing PAs exhibit impairment inphysical and emotional health as well as in occupational andfinancial functioning. They also have increased use of healthcare facilities, emergency departments, and psychoactive drugs
https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX[2]. A PA is defined as “an abrupt surge of intense fear or
discomfort that reaches a peak within minutes,” characterized
by rapid activation of stress-related physiology and fear-related
cognitions [3]. Although the body’s ability for rapid
physiological activation is evolutionarily advantageous (eg, toprovide a bodily response to a direct predatory attack [4]), thegeneration of this response during PAs, which are inherently
unexpected, impairs daily functioning [3].

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 1
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Quantifying PAs
Given their critical evolutionary role in the body’s acuteresponse to stress [5] and measurement feasibility, heart rate(HR) and respiration variables have been studied as key metrics
in PA research. For instance, HR was observed to increasesignificantly 1 minute before patient-identified PA onset andremain at a level significantly higher than the resting state forthe approximately a 10-minute duration of the PA [6]. However,other studies measuring HR during real-world PAs demonstrated
mixed results such that only a moderate percentage of examined
PAs (37%-68%) were found to exhibit rapid HR increases
compared with non-PA periods. Similarly, respiration changes
have been observed before PA and during the PA as
characterized by changes in tidal volume and end-tidal PCO2,
but these PA-related values did not differ significantly fromtypical resting states [7-9].
As PAs are unexpected and episodic, they are difficult to capture
in real-world situations. To collect and present sufficient data,
studies on PA physiology have (1) presented spontaneous PA
data as case studies [10,11], (2) induced PAs medically in thelaboratory [12], or (3) focused on a subsample of 4% of people
having PAs who have panic disorder (PD) [6,13], which ischaracterized by recurrent PAs with persistent worry between
PAs. There is evidence to suggest that PA physiology differs
in severity depending on how the PA is induced [12] andparticipant symptomatology [6]; thus, generalizations toreal-world PAs for individuals without PD are limited. The
sample size across studies is also relatively low. In a study ofindividuals with PD, 43 participants were monitored for 6
continuous days and 13 unexpected PAs were observed,
demonstrating that high effort and participation is necessary tocollect data on even a small number of episodic PAs. In the 3studies that aimed to capture the average physiological changes
during real-world PAs in individuals without PD, the combined
number of PAs was only 50 [7-9] including several PAs within
individuals. Thus, there is a clear need for additional studies ofPA physiology, particularly in individuals without PD.
A key factor that has likely limited previous studies of PA
physiology is the availability of technologies for providing
noninvasive measurements in real-world environments. For
instance, one of the HR studies described above conducted
continuous ambulatory monitoring with 9 wearable sensors
connected by wires to a data monitor worn in a fanny pack [6].
This approach imposes a significant burden on researchers andparticipants alike. Less cumbersome equipment does exist[14-16], yet these specialized devices can be expensive andburdensome to carry around, limiting accessibility. To better
capture a representative picture of real-world PAs, more feasible
and accessible tools are needed to measure PAs wherever andwhenever they occur.

Treating PAs
Although there is limited evidence demonstrating the real-time
physiological response to PAs, there is more research on theirtreatment. It is critical that individuals experiencing PAs haveaccess to feasible and evidence-based interventions as they tend
to seek help at significantly higher rates (46%) than individuals
with any other axis 1 disorder each year [2,17]. Despite
https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

McGinnis et al
increased treatment-seeking, only 18% of services delivered tothese individuals are based on evidence specific to PA reduction[2], as most treatments are intended for general mental healthimpairment (ie, anxiety). Existing PA treatments can be
categorized by their mechanism of action as follows: (1) actively
attempting to prevent physiological symptoms (avoidancetechniques) or (2) reframing the patient’s perspective of their
physiological symptoms (approach techniques).
Avoidance techniques, such as meditation or progressive muscle
relaxation, focus on preventing physiological symptoms at onset.
In individuals with general anxiety, avoidance techniques havebeen found to induce (not reduce) anxiety in a significant
number of patients (17%-54%) [18]. For PAs specifically,prescribed relaxation demonstrates poorer effectiveness andsignificantly higher attrition than other treatment options [19].
Psychopharmacological intervention (eg, benzodiazepines) is
another potential avoidance technique for treating PAs. Although
this approach effectively stops the physiological symptoms ofPAs, it does not decrease the likelihood of future PAs and has
serious clinical side effects (eg, dependence, rebound anxiety,and memory impairment) that reduce quality of life [20]. Thus,
there is little evidence demonstrating that avoidance techniques
are effective in addressing the underlying mechanisms that drive
PAs and some of these approaches have significant side effects.
Approach techniques aim to help patients think about their
physiological symptoms differently and confront them.
Psychotherapies such as cognitive behavioral therapy withinteroceptive exposure involve reframing panic-related thoughts
and learning to experience symptoms with less panic via in vivo
exposure (eg, by inducing PA symptoms, such as dizziness, ina session by spinning the patient in a chair). These therapies arevery effective in reducing PA frequency and severity [19] but
require a weekly 1-hour visit with a trained and licensed
clinician for 12 consecutive weeks. Unfortunately, access tothese evidence-based therapies is limited [1], which motivatesthe development of alternative approaches for treating PAs that
do not require the presence of a licensed mental healthprofessional.
Biofeedback is an approach technique in which some form of
involuntary
physiology(eg,
respiratory
rate,electroencephalogram, or HR) is continuously measured overtime and simultaneously displayed back to the user. During
biofeedback, the user is trained to improve their health bylearning to regulate internal bodily processes that typically occur
involuntarily [21]. Biofeedback with breathing training (BT) is
one of the most commonly studied biofeedback techniques fortreating PAs. This approach, which requires a specialized device(ie, capnometer) to measure breathing data and display it backto the patient in real time, helps patients learn how to raise or
lower their end-tidal PCO2. In 2 studies of patients with PD, a4-week biofeedback with BT intervention (two 17-minutesessions per day) was shown to be effective at reducing PA
frequency and severity [15,22] at 1 year follow-up. However,studies have shown that individuals with PD exhibit breathingirregularities even when not experiencing a PA [23] and thus
the effectiveness of this treatment may be limited to those with
PD [24]. Other forms of biofeedback without BT have been
JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 2
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
shown to be effective for treating more general anxiety [25,26]
and thus could also be effective for treating PAs in those without
PD. For example, a meta-analysis of 24 studies investigating
the impact of HR variability biofeedback on stress and anxiety
revealed a rather large effect (Hedges g=0.83) [26]. Althoughthese data suggest that biofeedback can improve anxiety-related
symptoms across physiology, treatment effectiveness specificto PAs in persons without PD remains unknown. Biofeedback
is thought to provide effective intervention for PAs because it
allows users to “feel more in control of their bodily reactions
and react less fearfully to them,” thus ending the cycle of panicby acquiring a sense of mastery [17]. In support of this theory,
perceived control was found to be associated with theeffectiveness of both biofeedback with BT and cognitive
behavioral therapy [27]. HR, but not respiration variables, hasbeen significantly related to patient-reported rating of fear of
losing control [6], indicating that HR may be an ideal choice
for such interventional strategies. HR is significantly higher
during PAs [6] than in a resting state for those who have PA,
but there is no evidence to suggest that individuals with PAs(but not PD) experience differences in HR during typical restingstates. Approach techniques that demonstrate long-term
reduction in panic and anxiety focus on confronting symptoms
when they are active, even attempting to reproduce similar

McGinnis et alsymptoms while in a therapist’s office (eg, interoceptive
therapy). Now that we have the technology to provide the tools
and guidance during an episodic PA, biofeedback could be
especially effective during a PA, when an individual can
immediately observe their HR fluctuations; however, this has
not yet been examined in PAs.
To address the unmet needs of (1) quantifying PAs in individuals
in more representative and larger samples and with less burdenand (2) offering an accessible, biofeedback-based treatment
option for PAs, our research team has developed a digital
therapeutic called PanicMechanic (Figure 1). This mobile health(mHealth) app can accurately and feasibly collect HR data
during a PA using only a smartphone and use these data toprovide HR-based biofeedback to users during their PAs [28].
This tool can profile remote PAs, which was limited bytechnology so far, but the usefulness of this novel intervention
still needs to be assessed to inform future efficacy trials. Thus,in this study, we aim to present our novel digital therapeutic
PanicMechanic and leverage it as a feasible and scalable datacollection tool to capture profiles of real-world PAs in a sample
of help-seeking PanicMechanic users (analysis 1) and conduct
a pilot study to assess the feasibility and usefulness of
PanicMechanic as a PA intervention in a convenience sampleof university students (analysis 2).

Figure 1. PanicMechanic mobile health app screens. The PanicMechanic mHealth app is available wherever and whenever a user experiences a PA
(screen 1). It provides biofeedback through objective measurement of HR during the PA (screen 2) and allows users to capture their perceived anxiety
throughout the PA and identify their behavioral and thought triggers (screens 3 and 4). These data are aggregated over time to allow users to track theirprogress and identify trends that may be helpful for preventing future PAs (screens 4 and 5).

Methods

the data collection and analysis procedures used to pilot
PanicMechanic as a PA intervention.

To address these aims, we first introduce PanicMechanic andthen describe the data collection and analysis procedures used
to capture the profiles of real-world PAs. Finally, we describe

PanicMechanic

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

PanicMechanic is a commercially available mHealth app(available on Android and iPhone operating system) developedby our team and released in April 2020 that guides users through
JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 3
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
their PAs (Figure 1). The app provides biofeedback throughobjective measurements of HR during the PA, which are enabled
via an analog to reflective photoplethysmography provided by
the smartphone’s camera and validated algorithms [29]. For
photoplethysmography, the camera is used to assess changes
in the color of the fingertip associated with blood being pumpedby the heart through the capillary bed of the fingertip. The app
also allows users to rate their anxiety levels throughout the PA
and choose from a selection of and record information about
lifestyle contributors and triggers (see Figure 2 for data froman example user’s PA). App features were developed by aclinical psychologist with experience in treating PAs, andleveraged best practices in clinical psychology while considering
input from a variety of individuals who undergo or who haveundergone PAs. Our previous work has demonstrated thefeasibility of this measurement modality during PAs [29,30].

McGinnis et al
Upon download, users complete a brief tutorial and areencouraged to open the app whenever they start to experiencea PA. During a PA, users are first instructed to place an index
finger on the lens of their phone camera with the flash activated
to record their HR for the first time. The app cycles throughscreens that display a graph of HR over time (biofeedback) andprompts the users to make additional HR measurements, rate
their anxiety level, and answer questions about lifestylecontributors (exercise, sleep, nutrition, stress, and substanceuse) and triggers for their PA. The screens provide encouraging
messages, such as “You got this!” throughout the attack andusing the data from previously logged attacks, the app also
provides an estimate of the time that remains in the user’s attack.
PanicMechanic aggregates user data over time to allow
identification of trends (eg, HR, anxiety ratings, and mostcommon triggers) that may help users prevent future attacks.

Figure 2. Example of panic attack case recorded with PanicMechanic. bpm: beats per minute; HR: heart rate. Data from a PA tracked by a PanicMechanicuser are reported here. The app tracked HR (bpm) and anxiety ratings (on a scale of 0 to 10; low to high) during attacks (top). This attack lasted forapproximately 5 minutes, during which time HR showed 3 distinct peaks and anxiety ratings generally decreased. The app also allowed users to identify
lifestyle factors and triggers that may have contributed to the attack (bottom). Data were tracked over time and reported back to the user (Figure 1) tohelp them identify trends that may be helpful for preventing future PAs.

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 4
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Capturing Profiles of Real-world PAs (Analysis 1)
To capture profiles of real-world PAs, we considered data from
PanicMechanic users in the 1-year period from April 2020 toApril 2021. All the users of the app agreed to its terms andconditions, which included the statement, “Your User Content
might be anonymized and used for research purposes.” As of
April 2021, PA data were available from 148 anonymized
PanicMechanic users.
Data from the first PA tracked by these users were used to
examine the average profile of HR and anxiety ratings
throughout an attack. The inclusion criteria for considering thefirst PA from a given user were that the HR time series had tohave at least 4 HR samples (80/148, 54.1% rejected) and a clear
peak (18/148, 12.2% rejected). These criteria led us to reject,for example, instances when app use was terminated before theend of the PA (before a peak HR occurred) or commenced after
the peak HR had occurred. These data were leveraged tocompute the ensemble average time series of HR and subjective
anxiety ratings to capture the physiological profile of real-world
PAs and explore their relationships during the PA.
We cannot control when users begin using the app during their
PA; hence, to compute the ensemble time series, it wasnecessary to express the time series relative to a standardized
instant in time. Thus, all time stamps were expressed relative
to the first peak in the HR signal as this was an easily
identifiable physiological feature, was expected to characterizea typical PA [6], and was common across many of the tracked
PAs. As the time stamps did not exactly match across the timeseries, it was necessary to linearly interpolate the HR data overa uniform grid (0.01-second intervals) for the time interval (−15and 60 seconds, ie, only data 15 seconds before and 60 seconds
after the first HR peak were considered) before averaging. Time
series were then smoothed using a low-pass filter (zero-phase,fourth order, Butterworth filter, and 0.1 Hz cutoff frequency)
and averaged. If a time series contained data only for timestamps within (but not the entire) a time interval, it was includedin the ensemble average only for those time stamps for whichdata were recorded or could be interpolated. For example, if atime series had data at time stamps −17, −5, 0, 20, and 33
seconds, it would contribute to the ensemble average only forthe subinterval (−15 and 33 seconds). Ensemble average timeseries of subjective anxiety rating data (n=48) were processedin the same way as the HR data (n=50) for PAs that met theinclusion criteria.
On capturing the profiles of real-world first recorded PAs, we
also extracted information from each PA about the typicalduration of recordings, severity (in terms of peak HR and anxiety
rating), and information about lifestyle contributors and triggers.
To provide a more comprehensive picture of these factors, weconsidered all the data from the first 4 PA recordings that met
the inclusion criteria from 148 PanicMechanic users.

PanicMechanic as a PA Intervention (Analysis 2)
To assess the feasibility and usefulness of PanicMechanic as a
PA intervention, we considered data from a pilot study of
PanicMechanic use among university students. This
demographic was chosen as a convenience sample because thehttps://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

McGinnis et al
majority of university students have smartphones and are open
to using mental health apps [31] and because PAs have beenshown to rise significantly during the transition to adulthood[32]. Email, social media, and flyer advertisements were used
to recruit 18 participants at a northeastern public university in
March 2020, April 2020, and May 2020, notably (andcoincidently) just after the COVID-19 pandemic–related
stay-at-home order was issued in the northeastern United States.
Interested persons were screened via phone for eligibility withthe following inclusion criteria: must own an Apple iPhone(Android version was not bug-free at the time of the study), be
at least 18 years old, report experiencing a PA in the past month,and have university student status.
The participants provided written informed consent before
completing a baseline assessment survey. They were given
access to PanicMechanic on their personal smartphones andviewed the app’s tutorial. They were instructed to use the app
whenever they experienced a PA during the following 12 weeks.
Throughout the study period, the participants completed
2-minute weekly web-based surveys about their PA and wereadministered a 15-minute web-based follow-up survey about
their experience with using the app 1 week after the study
period. Upon study completion, participants were compensatedwith Amazon gift cards worth up to US $50. All data collection
activities were approved by the institutional review board of
the University of Vermont (CHRBSS 00000747).
The baseline assessment survey included 42 items that captured
participant demographics; PA symptom assessment using theStructured Clinical Interview for Diagnostic and Statistical
Manual for Mental Disorders, which is a standardized
semistructured interview; and previous treatment and mental
health history. During the 2-minute weekly survey, if theparticipant self-reported capturing a PA with PanicMechanic,
they also rated how difficult it was to use the app and indicatedthe ways (if any) in which the app was helpful. The follow-up
survey included open-ended questions about helpfulness andchallenges of app use, an indication of whether the participant
would use the app in the future, and if they would recommend
the app to others who experience PAs.
Descriptive data for participants in the pilot study wereavailable, including demographic characteristics (N=18) andtheir PanicMechanic use (n=16; of the 18 participants, 2 [11%]participants did not participate beyond the first week of thestudy period and were lost to follow-up). Content analysis was
conducted on qualitative data responses to 2 open-ended itemsin the follow-up survey at the conclusion of the 12-weekintervention: “Overall, in what ways did you find app use
helpful” and “Overall, what challenges did you face in using
the app during panic attacks?” Content analysis followed thesteps detailed in a previous work [33], including first inductive
and then deductive analyses to understand the data. First, 2
raters independently conducted inductive content analysis thatinvolved reading all the participant responses, making notes on
their content, grouping them, and organizing the responses into
categories [33]. Next, the raters jointly conducted deductive
content analysis that involved developing a structured matrixof categories, coding the data into those categories, anddiscussing how those data compare to expectations [33]. Any
JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 5
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
discrepancies between the 2 raters were blindly analyzed by athird rater and majority ruled. Consensus categories,
descriptions, percentages of total responses, and example
responses are presented.

Results
Profiles of Real-world PAs (Analysis 1)
A total of 148 PanicMechanic users tracked at least some HR
data from at least one PA. In accordance with the iOS app
privacy policy, no demographic data were required to download
and use the app; thus, we were not able to report any
demographic data besides the knowledge that all 148 users had
unique and verified email addresses. PanicMechanic had beenfeatured on news media outlets in April 2020 when it became
available but had no paid advertising to promote it. Figure 2
provides an example of the data recorded by PanicMechanic
during a PA tracked by a user, including the time series of HR
and anxiety ratings (top), lifestyle contributors, and the PA
trigger (bottom). This example PA lasted just over 5 minutes,
during which time you could see a decreasing trend in anxiety
rating and 3 clear instances when the user’s HR peaked (atbetween 90 and 115 beats per minute [bpm]) and then returned
to baseline (at between 60 and 70 bpm). Before this attack,triggered by financial issues, the user reported much worse
exercise but slightly better diet, sleep, substance use, and stress

McGinnis et althan typical. It should be noted that significant heterogeneity
was observed in HR patterns across PanicMechanic users,
including in the timing, amplitude, and duration of HR
fluctuations and particularly following the relatively consistentpattern of initial HR peak and return to baseline.
Figure 3 (top) shows the mean HR for the first PA recorded by
50 users with at least 4 HR measurements demonstrating
activation (an increase to peak) and recovery (a decrease frompeak) slopes. The mean peak HR was approximately 98 (SD21.56) bpm. It appeared to take approximately 30 seconds frompeak HR for a significant recovery down to 85 bpm, which wasmaintained for an average of 30 seconds. Subjective anxiety
rating per minute (range 0-10) maintained a weak yet significant
recovery slope (E=−0.43, SE 0.001; P<.001) from peak HR to
PA end, which appears to be insensitive to specific changes in
HR. Figure 3 (bottom) also demonstrates individual
heterogeneity across the demeaned 50 recorded first PAs.
Several individual PAs appeared to show a secondary smaller
peak HR, which could indicate a cyclical HR pattern for a subset
of users that was also exhibited in the example PA presented
in Figure 2. The average PA recording lasted 4.64 (SD 6.27)
minutes. Exercise amount, stress level, sleep, and eating habitswere all slightly worse to typical and substance use was typicalto slightly better during the 24 hours before a PA (Figure 4).
The most commonly identified triggers out of the given choices
were health, conflict, performance, and workload (Figure 4).

Figure 3. Mean heart rate (HR) and anxiety level during panic attacks. bpm: beats per minute. This figure shows ensemble average HR (solid, left axis,
N=50) and subjective anxiety rating (dashed, right axis, N=48) responses for the first PA measured by PanicMechanic users (top). Both time series are
expressed relative to the first peak in the HR signal. Demeaned HR recordings (gray, bottom) used for computing the ensemble average (black, bottom)
demonstrate the variety in HR trajectories observed.

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 6
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

McGinnis et al

Figure 4. Frequencies of panic attack data. This figure shows participant-reported PA data including mean scores for potential lifestyle contributors
that may impact the likelihood of experiencing a PA and the frequency of triggers identified as being responsible for inducing the PA. Lifestyle contributor
responses were on a 5-point scale from a lot worse or less to a lot better or more compared with typical. Stress, diet, sleep quality, and exercise were
all reported as slightly worse, whereas substance use was reported as slightly better immediately before the PA. Thoughts about health were the mostcommon trigger followed by conflict, performance, and workload.

PanicMechanic as a PA Intervention (Analysis 2)
The demographics of the 18 study participants aged 19-35 years(Table 1) indicate that most of them identified as women, wereWhite, had diagnosed mental health disorder, and had previously
sought services for their panic. None of the participants hadreceived biofeedback previously. Participants cited an increase
in PA frequency, severity, and duration (mean 5.94, SD 2.38
on a scale of 1-10; 1=not at all increased, 5=somewhat increased,
10=extremely increased) owing to the COVID-19 pandemic,
which began shortly before the study recruitment commenced.
The most commonly reported panic symptom was a racing,pounding, or skipping heart (16/16, 100%) followed by shortnessof breath (13/16, 83%), trembling or shaking (12/16, 78%), andfeeling crazy or a loss of control (12/16, 78%; Figure 5). Overthe 3-month intervention, the following information wascollected in weekly surveys: 94% (15/16) of the participants
self-reported to have experienced 123 attacks, of which 39(31.7%) were self-reported as recorded in the app. The reasonsfor missing PA recordings are listed in Table 2. Of the 10participants, 9 (90%) participants with a valid recorded attack(recorded at least four HR data points, which would take about
60 seconds to record) indicated that it was helpful in at least
one of the following ways: (1) reducing the severity or durationof the PA, (2) learning about their personalized fear response,or (3) feeling more in control of their body. The participants

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

who used the app rated the user difficulty as 4.73 (SD 2.57) out
of 10.
Table 2 presents a qualitative analysis of participant commentsfrom the final survey (n=16). In response to an open-ended
question, “Overall, in what ways did you find app use helpful?”
the most frequent responses were about observing symptom
patterns, which aided understanding of body response (symptom
pattern recognition) and the structure of the app helping toredirect user attention (guided attention). In response to the
open-ended question, “Overall, what challenges did you facein using the app during panic attacks?” the most frequent
responses were about remembering or being motivated enoughto open the app (forgot or unmotivated to use) while
experiencing panic and app problems owing to technology glitch
or user error (such as difficulty placing finger properly for HR
to be detected) being a barrier to use (technology difficulties).
Representative testimonials and additional response categories
are presented in Table 2. When asked if they would use the app
again in spite of difficulties, 56% (9/16) of the participants
indicated they would. When asked if they would recommend
the app to someone else experiencing panic, 94% (15/16) of theparticipants indicated they would recommend it. In examining
the discrepancy between the prevalence of future use andrecommendation to others, it appeared that some participants
believed the app could be helpful to others who may better
remember to open the app at the start of panic symptoms.

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

McGinnis et al

Figure 5. Symptoms of panic attack among the participants. This figure shows that the most common PA symptom is racing HR, which was experienced
by 100% (16/16) of participants and supports our choice to consider HR biofeedback for PAs. Shortness of breath was the next most common symptom,
experienced by 83% (13/16) of participants. The number of responses range from 17 to 18 owing to some missing responses.

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 8
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

McGinnis et al

Table 1. Pilot study participant demographics (N=18).
Pilot study demographics

ValuesGender, n (%)
Female13 (72)Male

3 (17)Other

2 (11)

Age (years), mean (SD)a24 (5)

Race, n (%)
White

15 (83)

Asian American

1 (6)

Other

2 (11)

Ethnicity, n (%)
Non-Hispanic

16 (89)Hispanic

2 (11)

Mental health diagnosis, n (%)
Any diagnosis

13 (72)Type of diagnosis
Anxiety

10 (56)

Depression

7 (39)OCDb

3 (17)

PTSDc

3 (17)ADHDd

2 (11)Panic

2 (11)

Eating disorder

2 (11)

Bipolar disorder

1 (6)Adjustment disorder

1 (6)Personality disorder

1 (6)Panic treatment sought, n (%)

aCurrently in therapy

12 (67)

Ever in therapy

17 (94)

Physician13 (72)

Emergency room or urgent care

5 (28)

Prescribed medication

10 (56)

Self-medicated (alcohol or drugs)

5 (28)

Biofeedback

0 (0)Range: 19-35 years.

b

OCD: obsessive compulsive disorder.

c

PTSD: posttraumatic stress disorder.

d

ADHD: attention-deficit/hyperactivity disorder.

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 9
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

McGinnis et al

Table 2. Pilot study content analysis (n=16).
Qualitative category

Values, n(%)

Example testimonial 1

Example testimonial 2

Overall, in what ways did you find app use helpful?
Symptom pattern recognition (ob- 6 (38)serving symptom patterns aided theunderstanding of body response)“Rather than freaking out and feeling like I “I liked how it kept track of my heart rate
was dying, I saw that my heart rate was just because seeing it decrease was calming.”
slightly elevated and fluctuating, and I knewthat the attack was temporary and I could
work through it.”Guided attention (the structure redi- 6 (38)rected attention)“Watching my heart rate during the panic at- “Seeing my pulse change was really helpful
tack helped me focus more on what was go- in giving me something to focus on to calming on.”down during a panic attack.”

Accessibility (knowing it was acces- 3 (19)sible)“The app provides instant assistance with at- “The app was accessible from my pocket,
tacks, instead of waiting to get help.”
easy to read, and easy to follow.”

Physiological validation (the person- 3 (19)alized data objectively acknowledged the experience as a panic attack)“The app was most helpful in getting me to “I found the information provided by the app
acknowledge experiences that I've had for a regarding panic attacks to be calming andlong time as real, manageable symptoms of affirming.”
a known disorder - rather than just terrifying
feelings.”

Affirmations (the words of encouragement)

2 (13)“The positive affirmations it gives you is
helpful.”“It helped encourage me through it and stayin tune with myself.”

Triggers (being asked to identify
triggers)

2 (13)“Identifying the trigger and watching yourbody calm down as you calm down was
helpful.”“The app has many different triggers that we
could choose from.”

Overall, what challenges did you face in using the app during panic attacks?
Forgot or unmotivated to use (remembering or being motivated toopen the app owing to panic)

8 (50)“I often lacked the presence of mind or moti- “It is not my first instinct to use an app whenvation to get my phone and start tracking it.” I am having an attack.”

Technology difficulties (owing to
glitch or user error)

5 (31)“Sometimes I felt like it wasn't recording my “The app never gave me an average length
pulse right which I fixated on.”of my panic attacks so it always said I had 0
min left.”

Symptom barrier (panic symptoms
impacted app use once it was
opened)4 (25)“Physically shaking made it hard for me tokeep my finger on long enough to read myheart rate.”“I had trouble answering because I was
freaking out.”

Not accessible (did not have access 3 (19)to phone)“I didn't end up having my phone with meduring most of my panic attacks.”“It's too inconvenient for me to use considering my panic attacks often happen whiledriving.”

Repetitive guidance (structure ofapp was repetitive)

1 (6)“Questions too repetitive, especially whentracking more than one attack a day.”—a

9 (57)“To monitor myself and keep myself in touch “It was helpful so I would continue using it.”with my body and reality.”

15 (94)“I would say yes because it made me feel
more educated on my physical well-being.”

Would you use the app again?
Yes
Would you recommend the app?
Yesa“Although it doesn't work for me [Forgot toUse], I definitely recognize the benefit of realtime biofeedback, and I feel like this is a great
option for people who struggle with anxiety
and panic attacks.”

Second testimonial is not available.

Discussion
Summary
Overview
In this study, we examined the profile of real-world PAs
recorded by users of the mHealth app, PanicMechanic, in thehttps://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

largest observation of PA physiology yet considered in thisresearch area (N=50). We also assessed the feasibility andusefulness of the app as a PA intervention in a pilot study ofuniversity students (N=18). We discuss the results of thesestudies in the following sections, including an analysis of thecharacteristic changes in HR and anxiety ratings during PAs,
common lifestyle contributors and triggers for PAs, and the
JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
observations of the pilot study participants after using
PanicMechanic for the 12-week intervention period.

Profiles of Real-world PAs
During recorded PAs, HR appears to fluctuate by approximately
15 bpm (Figure 3), which is consistent with several studies thatreported significant HR change at the onset of a PA [6].
However, other studies with null results identified a HR increase
only when HR was disproportionate to activity levels [8]. Our
results show that the average HR varied between 85 and 100
bpm, which are within normal limits of resting HR for adults[34] and therefore would not qualify as HR increase under thatstudy criterion. It may be noted that although there is asignificant difference between PA HR peak and baseline, it is
the relative HR increase that indicates a PA. For reference, anincrease of approximately 15 bpm is seen in 30 seconds of stair
climbing in a healthy individual aged 44 years [35].
On the basis of data from the PanicMechanic users, there appears
to be an average duration from peak to baseline HR of
approximately 30 seconds during PAs (Figure 3). Given that
previous works indicate that PAs last approximately 10 minutes,
these results imply that there are several cycles of HR peaks
and recovery to baseline within a PA. Although the frequency
of HR changes during PAs has not been reported previously, a
study appears to show that 4 HR cycles occur within a typical
10-minute PA (Figure 2) [6]. Similarly, the data we report in
Figure 2 show 3 clear HR cycles during a 5-minute PA
recording. This cyclical HR pattern could be beneficial tobiofeedback intervention, allowing multiple chances for patients
to observe and expect recovery patterns. From the data presented
in Figure 2, we note that significant heterogeneity was observedin the pattern of HR during the recorded PAs across users.
Further exploration of this heterogeneity remains an interesting
potential area of future study as these HR patterns could further
inform our understanding of how and when PAs induce cyclesof physiological arousal.
Subjective anxiety did not follow a cyclical pattern, but slowly
decreased across the HR monitoring period (Figure 3). This isconsistent with research demonstrating that physiological data
does not necessarily correlate with subjective anxiety ratings
during fear tasks (eg, tasked with giving a spontaneous speechin front of judges [36]). In previous literature, a significant HR
increase was seen to precede self-reported PA onset byapproximately 1 minute [6]; thus, there may be a sequential
nonlinear relationship between HR and subjective anxiety
outside the scope of our recorded PA profile. Observing
moment-by-moment HR metrics alongside self-reported anxiety
may allow users, or, importantly, help clinicians to observe andexpect panic patterns that they may not be able to intervene onotherwise.
We also derived profiles of PA triggers (of the available
choices), which were not previously examined. As expected,
because this mHealth app became available during the
COVID-19 pandemic in the United States (released in April2020), health was the primary trigger in more than one-third ofPAs (Figure 4). This is supported by the pilot study data inwhich the participants indicated that the pandemic increased
the frequency, severity, and duration of their PAs. On days
https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

McGinnis et al
where PAs were recorded, the users reported slightly worse
stress, sleep, and eating habits; slightly less exercise than typical;
and slightly less drug or alcohol consumption in the past 24
hours (Figure 4). These results could inform future studies
focused on predictive models of PA occurrence by providing
objective monitoring of these parameters or behavioral
interventions to reduce PA risk.

PanicMechanic as a PA Intervention
The pilot study results provide insight into the populations thatare most interested in participating in an mHealth intervention
for PAs. Results show that most participants had multiple mental
health disorders (Table 1), consistent with previous literature
on individuals who experience PAs [37]. Of the 18 participants,
2 (11%) participants had PD (observed in 3.8% of persons with
PAs in the general public [1]). In addition, consistent withprevious literature, the participants (all experienced a PA in themonth before study participation) had also sought several formsof help services before the study [2] but, importantly, had nevertried biofeedback. Interestingly, all the participants reported
experiencing subjective HR increases during PAs (Figure 5),suggesting the validity of HR-based biofeedback.
We assessed the subjective feasibility of using PanicMechanic
as a biofeedback intervention for PAs. Quantitatively, theparticipants rated the difficulty of use as a 4.73 out of 10.
Qualitatively, half of the participants indicated that the maindifficulty of app use was not remembering or being motivatedto open the app during the onset of a PA. Despite difficulties,
56% (9/16) of the participants indicated that they would use theapp again in the future. Future development work to incorporate
hearables and wrist-worn or other wearable devices is indicatedto capture HR continuously and more seamlessly. We envisiona system in which a push notification asks the user to open the
PanicMechanic app if a HR increase has occurred outside of
exercise, which may precede a PA [6]. This type of systemmodification could, for example, reduce the challenge of notremembering to open the app at the beginning of a PA.
However, despite these challenges, 94% (15/16) of theparticipants would recommend the PanicMechanic app to others
who have PAs. Interestingly, several participants indicated thatthey would not use the app themselves but would recommend
it. These participants cited that they believed in the theory of
the intervention, but that their use challenges such as forgetting
to use the app or their own circumstances such as only
experiencing PAs while driving prevented them from benefittingfrom the intervention. Quantitatively and qualitatively, theparticipants found this biofeedback app useful. In weekly
reports, 90% (14/16) of the participants who had valid use of
the app (at least 4 HR data points, which would take at least 60
seconds to record) indicated that PanicMechanic was helpful
in one of the following three ways: (1) reducing the severity or
duration of the PA, (2) learning about their personalized fear
response, or (3) feeling more in control of their body.
Qualitatively, they reported that observing their personalized
symptom patterns aided their own understanding of their bodyand also that the app helped them focus by guiding them throughtheir panic in a structured way. These mechanisms are consistent
with previous theory on how biofeedback works [17] andJMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
indicate that PanicMechanic shows promise as an accessible
PA intervention.
There are several limitations to our study. First, we cannot becertain of the validity and duration of the recorded PAs. It is
possible that users recorded HR data outside of a PA, started torecord data after PA onset, and completed data recording before
the end of a PA. We attempted to mitigate these problems byproviding a step-by-step tutorial and practice mode on the app
upon download and only analyzing PAs that had at least 4 HR
measurements with a visible peak. Importantly, these steps
yielded exclusion of 66% of our HR data. Thus, althoughcollection of unsupervised data allows for much larger sample
sizes than previously studied, the available data diminishes as
it is cleaned [6-9]. As the app provided biofeedback during thefirst PA recording, it is possible that PAs may be attenuated inintensity owing to biofeedback, as the effective number ofsessions studied ranged from 1 to 50 [26]. In addition, we report
the prevalence of triggers of remote PAs. However, it isimportant to note that in this iteration of PanicMechanic, there
were specific choices of triggers and no ability to input otheroptions. Users were also able to track a PA without indicating

McGinnis et ala trigger. These prelisted triggers likely do not encompass all
the triggers of PAs and thus the results can only represent theprevalence of the given triggers. Given the likely need for
multiple sessions for PA improvement and the fact that only 5(31%) of the 16 participants in our pilot study used the app morethan twice, we were not able to evaluate the objective
effectiveness of PanicMechanic. This should be analyzed infuture studies of app users and specifically in a controlled trial.
Overall, our pilot study sample size was small and they
represented a subsample of all the people who have PAs. Futurestudies should include a larger sample with a more diverse rangeof people who have PAs to inform more generalizableconclusions.

Conclusions
Overall, we have presented a small window into the profile ofreal-world PAs in the largest sample of people who have PAs
to date. We also demonstrated promising preliminary resultsfrom a pilot study with participants indicating that
PanicMechanic is useful, albeit with some feasibility challenges,
which can be addressed with simple app improvements thatleverage existing technologies.

Acknowledgments
This study was supported by the University of Vermont Ventures funding. This study was also supported by the National Instituteof Mental Health Career Development Award to EM (MH123031-01A1).

Conflicts of Interest
RM and EM are cofounders of PanicMechanic Inc, which has commercialized the PanicMechanic mHealth app currently offeredon the Apple App Store and Google Play Store. This conflict is being managed by the University of Vermont. RM: stock ownershipfor Epicore Biosystems, Inc, and Impellia, Inc; consulting for HX Innovations Inc, University of Washington, and Happy Health,
Inc; research funding from MC10, Inc, Epicore Biosystems, Inc, Medidata, Inc. These conflicts are being managed by the
University of Vermont.

References
1.

2.
3.
4.

5.
6.
7.
8.
9.
10.
11.

Kessler RC, Chiu WT, Jin R, Ruscio AM, Shear K, Walters EE. The epidemiology of panic attacks, panic disorder, andagoraphobia in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2006 Apr;63(4):415-424 [FREE Fulltext] [doi: 10.1001/archpsyc.63.4.415] [Medline: 16585471]
Klerman GL, Weissman MM, Ouellette R, Johnson J, Greenwald S. Panic attacks in the community. Social morbidity andhealth care utilization. JAMA 1991 Feb 13;265(6):742-746. [Medline: 1990190]
Diagnostic and statistical manual of mental disorders. Oxford Bibliographies. URL: https://www.oxfordbibliographies.com/
view/document/obo-9780199828340/obo-9780199828340-0022.xml [accessed 2021-12-18]
Mobbs D, Marchant JL, Hassabis D, Seymour B, Tan G, Gray M, et al. From threat to fear: the neural organization of
defensive fear systems in humans. J Neurosci 2009 Sep 30;29(39):12236-12243 [FREE Full text] [doi:10.1523/JNEUROSCI.2378-09.2009] [Medline: 19793982]
Hall J, Hall M. Guyton and Hall Textbook of Medical Physiology E-Book. Amsterdam: Elsevier Health Sciences; 2020.
Meuret AE, Rosenfield D, Wilhelm FH, Zhou E, Conrad A, Ritz T, et al. Do unexpected panic attacks occur spontaneously?
Biol Psychiatry 2011 Nov 15;70(10):985-991 [FREE Full text] [doi: 10.1016/j.biopsych.2011.05.027] [Medline: 21783179]
Barr Taylor C, Telch MJ, Havvik D. Ambulatory heart rate changes during panic attacks. J Psychiatr Res 1982;17(3):261-266.
[doi: 10.1016/0022-3956(82)90004-8] [Medline: 7187688]
Taylor CB, Sheikh J, Agras WS, Roth WT, Margraf J, Ehlers A, et al. Ambulatory heart rate changes in patients with panic
attacks. Am J Psychiatry 1986 Apr;143(4):478-482. [doi: 10.1176/ajp.143.4.478] [Medline: 3953890]
Cameron OG, Lee MA, Curtis GC, McCann DS. Endocrine and physiological changes during "spontaneous" panic attacks.
Psychoneuroendocrinology 1987;12(5):321-331. [doi: 10.1016/0306-4530(87)90061-8] [Medline: 3432497]
Cohen AS, Barlow DH, Blanchard EB. Psychophysiology of relaxation-associated panic attacks. J Abnorm Psychol 1985
Feb;94(1):96-101. [doi: 10.1037//0021-843x.94.1.96] [Medline: 3980861]
Lader M, Mathews A. Physiological changes during spontaneous panic attacks. J Psychosom Res 1970 Dec;14(4):377-382.
[doi: 10.1016/0022-3999(70)90004-8] [Medline: 5495258]

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 12(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
12.
13.
14.
15.

16.

17.
18.
19.
20.
21.
22.
23.
24.

25.
26.
27.

28.
29.

30.

31.
32.

33.
34.

35.

Goetz R, Klein D, Gorman J. Consistencies between recalled panic and lactate-induced panic. Anxiety 1994;1(1):31-36.
[doi: 10.1002/anxi.3070010108] [Medline: 9160544]
Freedman RR, Ianni P, Ettedgui E, Puthezhath N. Ambulatory monitoring of panic disorder. Arch Gen Psychiatry 1985
Mar;42(3):244-248. [doi: 10.1001/archpsyc.1985.01790260038004] [Medline: 3977546]
Spire launches new app with breath monitoring, contextual feedback. MobiHealthNews. 2016. URL: https://www.
mobihealthnews.com/content/spire-launches-new-app-breath-monitoring-contextual-feedback [accessed 2021-12-18]
Kaplan A, Mannarino AP, Nickell PV. Evaluating the impact of Freespira on panic disorder patients' health outcomes andhealthcare costs within the allegheny health network. Appl Psychophysiol Biofeedback 2020 Sep;45(3):175-181. [doi:10.1007/s10484-020-09465-0] [Medline: 32342249]
Rubin J, Eldardiry H, Abreu R, Ahern S, Du H, Pattekar A, et al. Towards a mobile and wearable system for predicting
panic attacks. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
2015 Presented at: UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous
Computing; Sep 7 - 11, 2015; Osaka Japan. [doi: 10.1145/2750858.2805834]
Bouton ME, Mineka S, Barlow DH. A modern learning theory perspective on the etiology of panic disorder. Psychol Rev
2001 Jan;108(1):4-32. [doi: 10.1037/0033-295x.108.1.4] [Medline: 11212632]
Wells A. Panic disorder in association with relaxation induced anxiety: an attentional training approach to treatment. Behav
Ther 1990;21(3):273-280. [doi: 10.1016/s0005-7894(05)80330-2]
Craske MG, Brown TA, Barlow DH. Behavioral treatment of panic disorder: a two-year follow-up. Behav Ther
1991;22(3):289-304. [doi: 10.1016/s0005-7894(05)80367-3]
Pull C, Damsa C. Pharmacotherapy of panic disorder. Neuropsychiatr Dis Treat 2008 Aug;4(4):779-795 [FREE Full text][doi: 10.2147/ndt.s1224] [Medline: 19043522]
Bhat P. Efficacy of Alfa EEG wave biofeedback in the management of anxiety. Ind Psychiatry J 2010 Jul;19(2):111-114[FREE Full text] [doi: 10.4103/0972-6748.90341] [Medline: 22174533]
Meuret AE, Wilhelm FH, Ritz T, Roth WT. Feedback of end-tidal pCO2 as a therapeutic approach for panic disorder. J
Psychiatr Res 2008 Jun;42(7):560-568 [FREE Full text] [doi: 10.1016/j.jpsychires.2007.06.005] [Medline: 17681544]
Abelson JL, Weg JG, Nesse RM, Curtis GC. Persistent respiratory irregularity in patients with panic disorder. Biol Psychiatry
2001 Apr 01;49(7):588-595. [doi: 10.1016/s0006-3223(00)01078-7] [Medline: 11297716]
Wollburg E, Roth WT, Kim S. Effects of breathing training on voluntary hypo- and hyperventilation in patients with panic
disorder and episodic anxiety. Appl Psychophysiol Biofeedback 2011 Jun;36(2):81-91. [doi: 10.1007/s10484-011-9150-5][Medline: 21373936]
Moore NC. A review of EEG biofeedback treatment of anxiety disorders. Clin Electroencephalogr 2000 Jan;31(1):1-6.
[doi: 10.1177/155005940003100105] [Medline: 10638346]
Goessl VC, Curtiss JE, Hofmann SG. The effect of heart rate variability biofeedback training on stress and anxiety: ameta-analysis. Psychol Med 2017 Nov;47(15):2578-2586. [doi: 10.1017/S0033291717001003] [Medline: 28478782]
Meuret AE, Rosenfield D, Seidel A, Bhaskara L, Hofmann SG. Respiratory and cognitive mediators of treatment change
in panic disorder: evidence for intervention specificity. J Consult Clin Psychol 2010 Oct;78(5):691-704 [FREE Full text][doi: 10.1037/a0019552] [Medline: 20873904]
Biofeedback and anxiety. Psychiatric Times. 1999. URL: https://www.psychiatrictimes.com/view/biofeedback-and-anxiety[accessed 2021-12-18]
McGinnis RS, McGinnis EW, Petrillo C, Ferri J, Scism J, Price M. Validation of smartphone based heart rate tracking for
remote treatment of panic attacks. IEEE J Biomed Health Inform 2021 Mar;25(3):656-662. [doi: 10.1109/JBHI.2020.3001573][Medline: 32750933]
McGinnis R, McGinnis E, Petrillo C, Price M. Mobile biofeedback therapy for the treatment of panic attacks: a pilot
feasibility study. In: Proceedings of the IEEE 16th International Conference on Wearable and Implantable Body Sensor
Networks (BSN). 2019 Presented at: IEEE 16th International Conference on Wearable and Implantable Body Sensor
Networks (BSN); May 19-22, 2019; Chicago, IL, USA. [doi: 10.1109/BSN.2019.8771040]
Kern A, Hong V, Song J, Lipson SK, Eisenberg D. Mental health apps in a college setting: openness, usage, and attitudes.
Mhealth 2018 Jun 30;4:20 [FREE Full text] [doi: 10.21037/mhealth.2018.06.01] [Medline: 30050916]
Copeland WE, Angold A, Shanahan L, Costello EJ. Longitudinal patterns of anxiety from childhood to adulthood: theGreat Smoky Mountains Study. J Am Acad Child Adolesc Psychiatry 2014 Jan;53(1):21-33 [FREE Full text] [doi:10.1016/j.jaac.2013.09.017] [Medline: 24342383]
Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs 2008 Apr;62(1):107-115. [doi:10.1111/j.1365-2648.2007.04569.x] [Medline: 18352969]
Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate:
relations to age and gender over nine decades. J Am Coll Cardiol 1998 Mar 01;31(3):593-601 [FREE Full text] [doi:10.1016/s0735-1097(97)00554-8] [Medline: 9502641]
Teh KC, Aziz AR. Heart rate, oxygen uptake, and energy cost of ascending and descending the stairs. Med Sci Sports Exerc
2002 Apr;34(4):695-699. [doi: 10.1097/00005768-200204000-00021] [Medline: 11932581]

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

McGinnis et al

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 13(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
36.
37.

McGinnis et al

Takahashi T, Ikeda K, Ishikawa M, Kitamura N, Tsukasaki T, Nakama D, et al. Anxiety, reactivity, and social stress-induced
cortisol elevation in humans. Neuro Endocrinol Lett 2005 Aug;26(4):351-354. [Medline: 16136010]
Kessler RC, Stang PE, Wittchen H, Ustun TB, Roy-Burne PP, Walters EE. Lifetime panic-depression comorbidity in the
National Comorbidity Survey. Arch Gen Psychiatry 1998 Sep;55(9):801-808. [doi: 10.1001/archpsyc.55.9.801] [Medline:
9736006]

Abbreviations
bpm: beats per minute
BT: breathing training
HR: heart ratemHealth: mobile health
PA: panic attack
PD: panic disorder

Edited by G Eysenbach; submitted 17.08.21; peer-reviewed by J Callan, A Teles; comments to author 05.11.21; revised version
received 17.11.21; accepted 23.11.21; published 03.02.22
Please cite as:
McGinnis E, O'Leary A, Gurchiek R, Copeland WE, McGinnis R
A Digital Therapeutic Intervention Delivering Biofeedback for Panic Attacks (PanicMechanic): Feasibility and Usability StudyJMIR Form Res 2022;6(2):e32982
URL: https://formative.jmir.org/2022/2/e32982
doi: 10.2196/32982
PMID:©Ellen McGinnis, Aisling O'Leary, Reed Gurchiek, William E Copeland, Ryan McGinnis. Originally published in JMIR Formative
Research (https://formative.jmir.org), 03.02.2022. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The
complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright andlicense information must be included.

https://formative.jmir.org/2022/2/e32982

XSL• FO
RenderX

JMIR Form Res 2022 | vol. 6 | iss. 2 | e32982 | p. 14(page number not for citation purposes)