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Effects and Implementation of a Mindfulness and Relaxation App for Cancer Patients: Mixed-Methods Feasibility Study (Preprint)

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  • Institute for Complementary and Integrative Medicine
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Background Cancer diagnosis and cancer treatment can cause high levels of distress, which is often not sufficiently addressed in standard medical care. Therefore, a variety of supportive nonpharmacological treatments have been suggested to reduce distress in patients with cancer. However, not all patients use these interventions because of limited access or lack of awareness. To overcome these barriers, mobile health may be a promising way to deliver the respective supportive treatments. Objective The aim of this study is to evaluate the effects and implementation of a mindfulness and relaxation app intervention for patients with cancer as well as patients’ adherence to such an intervention. Methods In this observational feasibility study with a mixed methods approach, patients with cancer were recruited through the web and through hospitals in Switzerland. All enrolled patients received access to a mindfulness and relaxation app. Patients completed self-reported outcomes (general health, health-related quality of life, anxiety, depression, distress, mindfulness, and fear of progression) at baseline and at weeks 4, 10, and 20. The frequency of app exercise usage was gathered directly through the app to assess the adherence of patients. In addition, we conducted interviews with 5 health professionals for their thoughts on the implementation of the app intervention in standard medical care. We analyzed patients’ self-reported outcomes using linear mixed models (LMMs) and qualitative data with content analysis. ResultsA total of 100 patients with cancer (74 female) with a mean age of 53.2 years (SD 11.6) participated in the study, of which 25 patients used the app regularly until week 20. LMM analyses revealed improvements in anxiety (P=.04), distress (P
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Original Paper
Effects and Implementation of a Mindfulness and Relaxation App
for Patients With Cancer: Mixed Methods Feasibility Study
Michael Mikolasek1, MSc; Claudia Margitta Witt1,2,3, MBA, MD; Jürgen Barth1, PhD
1Institute for Complementary and Integrative Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
2Institute for Social Medicine, Epidemiology and Health Economics, Charité, Universitätsmedizin Berlin, Berlin, Germany
3Center for Integrative Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
Corresponding Author:
Michael Mikolasek, MSc
Institute for Complementary and Integrative Medicine
University Hospital Zurich and University of Zurich
Sonneggstrasse 6
Zurich, 8091
Switzerland
Phone: 41 44 255 51 49
Fax: 41 44 255 43 94
Email: michael.mikolasek@usz.ch
Abstract
Background: Cancer diagnosis and cancer treatment can cause high levels of distress, which is often not sufficiently addressed
in standard medical care. Therefore, a variety of supportive nonpharmacological treatments have been suggested to reduce distress
in patients with cancer. However, not all patients use these interventions because of limited access or lack of awareness. To
overcome these barriers, mobile health may be a promising way to deliver the respective supportive treatments.
Objective: The aim of this study is to evaluate the effects and implementation of a mindfulness and relaxation app intervention
for patients with cancer as well as patients’ adherence to such an intervention.
Methods: In this observational feasibility study with a mixed methods approach, patients with cancer were recruited through
the web and through hospitals in Switzerland. All enrolled patients received access to a mindfulness and relaxation app. Patients
completed self-reported outcomes (general health, health-related quality of life, anxiety, depression, distress, mindfulness, and
fear of progression) at baseline and at weeks 4, 10, and 20. The frequency of app exercise usage was gathered directly through
the app to assess the adherence of patients. In addition, we conducted interviews with 5 health professionals for their thoughts
on the implementation of the app intervention in standard medical care. We analyzed patients’self-reported outcomes using linear
mixed models (LMMs) and qualitative data with content analysis.
Results: A total of 100 patients with cancer (74 female) with a mean age of 53.2 years (SD 11.6) participated in the study, of
which 25 patients used the app regularly until week 20. LMM analyses revealed improvements in anxiety (P=.04), distress
(P<.001), fatigue (P=.01), sleep disturbance (P=.02), quality of life (P=.03), and mindfulness (P<.001) over the course of 20
weeks. Further LMM analyses revealed a larger improvement in distress (P<.001), a moderate improvement in anxiety (P=.001),
and a larger improvement in depression (P=.03) in patients with high levels of symptoms at baseline in the respective domains.
The interviews revealed that the health professionals perceived the app as a helpful addition to standard care. They also made
suggestions for improvements, which could facilitate the implementation of and adherence to such an app.
Conclusions: This study indicates that a mindfulness and relaxation app for patients with cancer can be a feasible and effective
way to deliver a self-care intervention, especially for highly distressed patients. Future studies should investigate if the appeal of
the app can be increased with more content, and the effectiveness of such an intervention needs to be tested in a randomized
controlled trial.
(JMIR Cancer 2021;7(1):e16785) doi: 10.2196/16785
KEYWORDS
mobile app; mobile phone; mindfulness; relaxation; cancer; qualitative research; implementation science; mHealth; evaluation
study; patient compliance; patient participation; patient preference
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Introduction
Background
Cancer diagnosis and subsequent medical treatments can cause
high levels of distress [1-4]. However, adequate psychological
support for patients with cancer is often lacking in standard
medical care [5,6]. Therefore, a variety of supportive treatments
have been suggested to reduce distress in patients with cancer,
such as mind-body medicine (MBM) [7]. MBM combines
various effective treatments such as mindfulness meditation,
relaxation, yoga, and tai chi [7,8]. Such MBM treatments can
have beneficial effects on cancer-related symptoms, such as
pain, fatigue, and sleep disturbance [9-11]. Furthermore, MBM
treatments can have beneficial effects on the quality of life of
patients with cancer [12-14]. These treatments can be provided
through guided MBM programs for patients with cancer, where
the patients learn various exercises (eg, physical exercises,
relaxation, and stress reduction) and are encouraged to practice
these newly learned exercises at home [15,16].
However, the uptake of supportive treatments in distressed
patients with cancer is moderate [17]. Barriers for the uptake
of such treatments include stigmatization, unawareness of such
interventions, or limited access [18,19]. This is problematic
because untreated, elevated levels of distress can lead to
additional negative effects, such as reduced quality of life, daily
functioning, and lower adherence to medical treatment [20,21].
Access can be restricted, for instance, because of geographical
distance, lack of treatment providers or knowledge thereof, and
financial constraints [22-24]. To overcome these limitations in
access, eHealth and mobile health (mHealth) interventions have
been proposed. eHealth is defined more broadly as the delivery
of health services or information through the internet and related
technologies [25], whereas mHealth uses mobile technologies
such as smartphones for the delivery of health services [26]. So
far, research indicates that eHealth interventions with
mindfulness or relaxation components can have beneficial
effects on health outcomes in various patient populations
[27-29]. However, eHealth studies focusing on patients with
cancer have shown inconsistent results [30,31]. Nonetheless,
eHealth interventions seem promising because they can have
positive effects on the well-being of patients with cancer [31].
Although mHealth interventions have some advantages over
web-based eHealth interventions (eg, more flexible access
because of mobility, the possibility of reaching a large number
of patients because of the large popularity of smartphones),
little is known about the best practices for the implementation
of mHealth interventions [32,33]. In addition, mHealth research
so far indicates that the adoption of mHealth interventions by
health professionals and patients can be inhibited by various
factors, such as perceived usefulness and ease of use [34,35].
Furthermore, there is a lack of mHealth studies with mindfulness
or relaxation-based interventions [27]. Therefore, we developed
a research app to conduct a feasibility study of a mindfulness-
and relaxation-based mHealth intervention for patients with
cancer [36]. The app included 3 exercises, namely, mindfulness
meditation, guided imagery, and progressive muscle relaxation.
Objectives
The aim of this study is to assess the feasibility of this mHealth
intervention using the Reach, Effectiveness, Adoption,
Implementation, and Maintenance (RE-AIM) evaluation
framework, which was developed for the evaluation of public
health interventions [37]. Although the results for the reach of
the dimensions, adoption over the course of 10 weeks, and
maintenance were published elsewhere [36], the present analyses
focus on the 3 dimensions of effectiveness, adoption, and
implementation over the course of 20 weeks to assess the
pre-post effects of the app on a variety of health outcomes and
adherence to the app intervention. In doing so, we investigate
whether such an app may be a beneficial, supportive care tool
for patients with cancer.
Methods
Study Design
For this feasibility study, we used a mixed methods approach.
For quantitative data, we assessed 4 paper-and-pencil
questionnaires that were sent to patients with cancer at baseline
and at weeks 4, 10, and 20. Demographics and patient
characteristics were assessed at baseline, and health outcomes
(physical, mental, and social health, health-related quality of
life, anxiety, depression, distress, mindfulness, and fear of
progression) were assessed over the 4 time points. Qualitative
data consisted of semistructured interviews with 5 health
professionals. In those interviews, we inquired about health
professionals’ perspectives on a mindfulness- and
relaxation-based mHealth intervention for patients with cancer
and its implementation in standard medical care. To receive
feedback from different health professionals, we conducted 2
face-to-face group interviews (1 interview with 2 nursing experts
and the second interview with 2 psychologists providing MBM
treatment for patients with cancer) and 1 individual interview
with an oncologist. All interviewees received access to the app
before the interview and could test the app. The interviewer
also demonstrated the app and its content to the interviewees
before the interview started.
To assess the feasibility of our mHealth intervention, we used
the RE-AIM implementation science framework [37]. Ethical
approval for the study was granted in April 2016 by the cantonal
ethics committee Zurich (BASEC-Nr. 2016-00258), and we
registered the study in the German Clinical Trials Register
(DRKS00010481).
Participants
Patients were eligible if they (1) had any cancer diagnosis at
any stage of cancer, (2) were aged 18 years or older, and (3)
owned either an iPhone (Apple Inc). or an Android-based
smartphone with at least a weekly connection to the internet.
Patients were excluded if they had suicidal ideation or
insufficient German language skills, if they intended to move
to another country, or if they had insufficient knowledge on
how to use a smartphone. The patient recruitment process is
described in detail elsewhere [36]. For the interviews with health
professionals, we invited experts (an oncologist, nursing experts,
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and psychologists) from the University Hospital Zurich, who
provide health care for patients with cancer.
App Intervention
All enrolled patients received the mindfulness and relaxation
app, which was specifically developed for this study and only
available for patients participating in the study. The app could
be downloaded in the Apple iTunes store and Google Play Store
for Android devices and accessed with a code, which was
provided to the patients after study inclusion. The app offered
3 exercises: mindfulness meditation, guided imagery, and
progressive muscle relaxation. The exercises were included in
the app as audio files with a duration of approximately 15
minutes each, and the patients could choose between a female
or male narrator. Patients were free to choose which exercises
they wanted to use and how often they wanted to practice.
However, we recommended to the patients to use an exercise
of their choice on a daily basis, ideally 5 times per week. To
help patients practice regularly, the app included an optional
notification feature that patients could set up to receive a daily
push notification on the mobile device, reminding them to
practice at an individually set time. Information about the use
of exercises (exercise type, date, and start and end times) was
saved in the backend and was only accessible to the researchers
as an XML log file. More information about the app is presented
in a previously published paper [36].
Outcomes
Effects
As we conducted a single-arm study without a control group,
we were not able to assess the effectiveness of the app
intervention. Therefore, for the RE-AIM dimension
effectiveness, we looked into pre-post effects in a variety of
health outcomes relevant to patients with cancer. We assessed
physical, mental, and social health using the Patient-Reported
Outcomes Measurement Information System (PROMIS 29)
[38]. PROMIS 29 is a 29-item scale assessing 7 health domains:
physical function (Cronbach α=.81), fatigue (Cronbach α=.94),
pain interference (Cronbach α=.96), depressive symptoms
(Cronbach α=.85), anxiety (Cronbach α=.81), ability to
participate in social roles and activities (Cronbach α=.88), and
sleep disturbance (Cronbach α=.86) with 4 items, each on a
5-point scale, and pain intensity with a single item on a 10-point
numeric rating scale.
For the assessment of health-related quality of life for patients
with cancer, we administered the Functional Assessment of
Cancer Therapy—General (FACT-G) [39,40]. The FACT-G
consists of 4 subscales: physical well-being (Cronbach α=.85),
social well-being (Cronbach α=.76), emotional well-being
(Cronbach α=.70), and functional well-being (Cronbach α=.79),
measured with 27 items on a 5-point scale. A higher score
indicates a better quality of life.
For the assessment of distress, we administered the Distress
Thermometer [41]. The Distress Thermometer is a numeric
rating scale, ranging from 0 to 10. A score of 5 or higher is
considered to indicate clinically relevant distress [42].
For the assessment of mindfulness, we administered the short
version of the Freiburg Mindfulness Inventory (FMI) [43]. The
FMI (Cronbach α=.87) assesses mindfulness with 14 items on
a 4-point scale, with a higher score indicating higher
mindfulness.
We measured anxiety and depression using the Hospital Anxiety
and Depression Scale (HADS). The HADS assesses 7 items for
the subscales anxiety (Cronbach α=.79) and depression
(Cronbach α=.67) on a 4-point scale, with a maximum score of
21 for each subscale. A score of up to 7 is considered normal,
a score between 8 and 11 is considered borderline, and a score
above 11 is considered caseness [44].
For the assessment of fear of progression, we administered the
Fear of Progression Questionnaire-Short Form (FoP-Q-SF) [45].
The FoP-Q-SF (Cronbach α=.81) consists of 12 items with a
5-point scale. A higher score indicates a greater fear of
progression.
We assessed PROMIS 29, FACT-G, and FMI at baseline and
at weeks 4, 10, and 20 and HADS, FoP-Q-SF, and Distress
Thermometer at baseline and at weeks 10 and 20. We defined
a continuous app user as a patient who regularly used the app
exercises (at least one exercise per week). We counted an
exercise as completed if the patient played the exercise audio
file for at least 10 minutes of the total time of 15 minutes. We
defined an intervention dropout as a patient who stopped using
the exercises for 4 consecutive weeks because regular practice
might be a prerequisite for a beneficial intervention. We defined
the first week when the patient stopped using the exercises as
a dropout week. A patient who never used an app exercise was
counted as a week 1 intervention dropout.
Adoption
For the RE-AIM dimension adoption, we looked at the number
of completed app exercises over 20 weeks and app exercise
preferences. We reported the median of completed app exercises
by all enrolled patients per week as well as the median of
completed app exercises by continuous app users. For exercise
preferences, we reported frequencies of used exercises for all
enrolled patients, stratified by gender of the patient and the
narrator.
Implementation
For the RE-AIM dimension implementation, we reported results
from interviews with health professionals regarding their opinion
on the implementation of the app intervention in addition to
standard medical care. In the interviews, we inquired about the
general impression regarding the app, implementation of the
app as an addition to standard medical care, and suggestions
for improvements.
Sample Size
One aspect evaluated in our feasibility study was the
characteristics and number of patients with cancer who
participated in the study (evaluation dimension reach), which
was reported previously [36]. Therefore, we did not perform an
a priori analysis to determine the required sample size for
adequate power. However, we aimed to recruit at least 100
patients, which is sufficient to achieve 80% power for a
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two-tailed t test with an α level set at .05 and a small effect size
of Cohen dof 0.28.
Data Analysis
Quantitative Data
All printed case report forms were entered by trained researchers
into the electronic database REDCap (Research Electronic Data
Capture), which was hosted at the University Hospital Zurich.
All analyses were carried out in SPSS version 25.0 (IBM Corp).
For baseline characteristics of patients, we used descriptive
statistics (frequencies and percentages for categorical variables
and mean and SD for continuous variables). For the analyses
of pre-post effects, we used linear mixed models (LMMs) to
analyze changes over time (baseline, week 4, week 10, and week
20) in health outcomes as well as differences between
continuous app users and intervention dropouts in health
outcomes. All patients who provided baseline data were included
in the analyses, and because we used LMMs, patients with
missing data in weeks, 4, 10, and 20 questionnaires were
included. The dependent variables were the 7 PROMIS 29
domains, FACT-G, HADS subscales anxiety and depression,
Distress Thermometer, FMI, and FoP-Q-SF. Furthermore, we
looked at the changes in the respective health outcomes for
subsamples with high distress (Distress Thermometer score 5),
high anxiety (HADS anxiety score of 8), and high depression
(HADS depression score of 8). As a covariance type, we used
an autoregressive covariance structure (AR1). Time was
included as a fixed effect. For group analyses, (continuous app
users vs intervention dropouts), we added group and
time-by-group as fixed effects. Hedge geffect sizes were
calculated as mean differences (baseline and week 20) divided
by pooled SDs for each health outcome of interest.
Qualitative Data
For the dimension implementation, we recorded the interviews
and transcribed the interviews verbatim. We used thematic
coding for structuring the interviews using MAXQDA 11
(VERBI Software), and we used content analysis according to
Mayring [46].
Results
Patient Characteristics
Between June 2016 and December 2018, we were able to recruit
100 patients with cancer, all of whom provided baseline
information. At week 20, 72 (72%) patients completed
questionnaire 4 (Figure 1). Baseline characteristics of all
enrolled patients (N=100) as well as subsamples of patients
with high distress (62/100, 62%), high anxiety (35/100, 26%),
and high depression (20/100, 20%) are summarized in Table 1.
Most patients (74/100, 74%) were female. The mean age of all
patients was 53.24 (SD 11.55) years, ranging from 23 to 84
years. Patients predominantly owned an iPhone smartphone
(67/100, 67%), whereas 30 patients (30/100, 30%) owned an
Android smartphone, and a few (3/100, 3%) owned both.
Figure 1. Flowchart.
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Table 1. Demographics for the total sample and high distress, high depression, and high anxiety subsamples.
High depressioncsubsample
(n=20)
High anxietybsubsample
(n=35)
High distressasubsample
(n=62)
Total sample
(N=100)
Patient demographics
Gender, n (%)
15 (75)26 (74)48 (77)74 (74)Female
5 (25)9 (26)14 (23)26 (26)Male
51.74 (11.63)51.22 (10.67)52.74 (10.67)53.24 (11.55)Age (years), mean (SD)
Type of cancer, n (%)
8 (40)18 (51)27 (44)39 (39)Breast cancer
3 (15)2 (6)7 (11)9 (9)Colon cancer
0 (0)2 (6)3 (5)6 (6)Ovarian or cervical cancer
1 (5)0 (0)3 (5)6 (6)Lung cancer
8 (40)13 (37)22 (35)40 (40)Others
Status of cancer treatment, n (%)
11 (55)24 (69)33 (53)46 (46)Total removal
5 (25)6 (17)15 (24)25 (25)Recurrence or incomplete removal
2 (10)1 (3)1 (2)3 (3)Uncertain
4 (20)4 (11)13 (21)26 (26)Other
Highest education, n (%)
0 (0)2 (6)2 (3)3 (3)Primary school
5 (25)5 (14)16 (26)22 (22)Apprenticeship
7 (35)14 (40)21 (34)41 (41)Secondary education
7 (35)14 (40)22 (35)33 (33)University degree
1 (5)0 (0)1 (2)1 (1)Unknown
aDistress Thermometer score 5.
bHospital Anxiety and Depression Scale anxiety score 8.
cHospital Anxiety and Depression Scale depression score 8.
Effects
The health outcome values at baseline and at week 20 as well
as effect sizes for the total sample and the high distress, high
anxiety, and high depression subsamples are presented in Table
2. Baseline distress was 5.29 (SD 2.31); therefore, patients were
on average above an assumed clinically relevant threshold of
5, with 62% of patients (62/100) reporting a distress level of 5
or higher. At week 20, distress decreased to an average of 4.1
(SD 2.12; Hedge g=0.53). The mean HADS anxiety score at
baseline was 6.88 (SD 3.50) and dropped to 6.31 (SD 3.78;
Hedge g=0.16) at week 20. Overall, 35% (35/100) of patients
reported an elevated HADS anxiety score (8) at baseline (mean
10.71, SD 1.95), which dropped to 8.85 (SD 3.50; Hedge
g=0.68) at week 20. For HADS depression, the mean score at
baseline was 4.96 (SD 2.78) and dropped to 4.55 (SD 3.31;
Hedge g=0.14) at week 20. Overall, 20% (20/100) of patients
reported an elevated HADS depression score (8) at baseline
(mean 9.00, SD 1.12), which dropped to 8.85 (SD 3.50; Hedge
g=0.61) at week 20. For the remaining measures without a
proposed threshold (PROMIS, FACT-G, FMI, and FoP-Q-SF),
changes from baseline to week 20 were small, with Hedges g
effect sizes ranging from 0.04 to 0.33.
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Table 2. Mean values of health outcomes at baseline and week 20, response rate (n), and effect sizes (N=100).
Hedges g(95% CI)Week 20BaselineSample and outcome
nMean (SD)nMean (SD)
Total sample
0.16 (0.46 to 0.15)716.31 (3.78)996.88 (3.50)
HADSaanxiety
0.14 (0.44 to 0.17)714.55 (3.31)1004.96 (2.78)HADS depression
0.53 (0.84 to 0.22)714.10 (2.12)995.29 (2.31)Distress
0.04 (0.34 to 0.27)7146.30 (7.32)9946.55 (6.54)
PROMIS physfunctb
0.15 (0.45 to 0.16)7055.01 (6.83)9955.97 (6.46)
PROMIS anxietyc
0.18 (0.49 to 0.12)7153.88 (7.81)10055.20 (6.81)
PROMIS depressiond
0.38 (0.69 to 0.07)7052.40 (10.31)9956.11 (9.23)
PROMIS fatiguee
0.23 (0.53 to 0.08)7049.52 (8.02)10051.44 (8.85)
PROMIS sleepf
0.18 (0.12 to 0.49)7149.84 (7.87)9948.42 (7.64)
PROMIS socialg
0.10 (0.41 to 0.21)7051.96 (9.38)9752.88 (9.10)
PROMIS painh
0.29 (0.02 to 0.59)7079.62 (14.81)9975.54 (13.85)
FACT-Gi
0.51 (0.20 to 0.83)6941.80 (6.42)9638.46 (6.62)
FMIj
0.13 (0.45 to 0.19)6430.28 (7.99)9331.33 (7.83)
FoPk
High distressl
1.36 (1.79 to 0.94)464.39 (2.19)626.79 (1.36)Distress
High anxietym
0.69 (1.20 to 0.16)268.85 (3.50)3510.71 (1.95)HADS anxiety
High depressionn
0.61 (1.27 to 0.05)177.47 (3.52)209.00 (1.12)HADS depression
aHADS: Hospital Anxiety Depression Scale; negative effect=improvement.
bPROMIS physfunct: Patient-Reported Outcomes Measurement Information System Physical Function; positive effect=improvement.
cPROMIS anxiety: Patient-Reported Outcomes Measurement Information System Anxiety; negative effect=improvement.
dPROMIS depression: Patient-Reported Outcomes Measurement Information System Depression; negative effect=improvement.
ePROMIS fatigue: Patient-Reported Outcomes Measurement Information System Fatigue; negative effect=improvement.
fPROMIS sleep: Patient-Reported Outcomes Measurement Information System Sleep Disturbance; negative effect=improvement.
gPROMIS social: Patient-Reported Outcomes Measurement Information System Ability to Participate in Social Roles and Activities; positive
effect=improvement.
hPROMIS pain: Patient-Reported Outcomes Measurement Information System Pain Interference; negative effect=improvement.
iFACT-G: Functional Assessment of Cancer Therapy—General; positive effect=improvement.
jFMI: Freiburg Mindfulness Inventory; positive effect=improvement.
kFoP: Fear of Progression; negative effect=improvement.
lDistress Thermometer score 5; negative effect=improvement; n=62.
mHADS anxiety score 8; negative effect=improvement; n=35.
nHADS depression score 8; negative effect=improvement; n=20.
The results for effects over time are presented in Table 3. LMM
analyses revealed that there was a significant decrease over time
in distress (P<.001), fatigue (P=.01), sleep disturbance (P=.02),
and anxiety (P=.04) measured with the HADS. Furthermore,
there was a significant increase in quality of life (P=.03) and
mindfulness (P<.001). No significant effects were found for
physical functioning, anxiety measured with PROMIS,
depression, ability to participate in social roles and activities,
and fear of progression. LMM analyses for the subsamples
revealed that distress decreased significantly in the high distress
subsample (P<.001), anxiety decreased significantly in the high
anxiety subsample (P=.001), and depression decreased
significantly in the high depression subsample (P=.03).
Dose-response analyses using LMMs with group-by-time
revealed no significant results.
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Table 3. Linear mixed models: estimates of fixed effect of time on health outcomes from baseline to week 20.
Estimates of fixed effects (time)Sample and dependent variable
Pvaluettest (df)Estimate (95% CI)
Total sample (N=100)
.042.04 (201.95)0.40 (0.79 to 0.01)
HADSaanxiety
.091.71 (206.42)0.29 (0.62 to 0.04)HADS depression
<.0013.96 (325.86)0.41 (0.62 to 0.21)Distress
.66.45 (318.35)0.13 (0.68 to 0.43)
PROMIS physfunctb
.161.42 (325.74)0.46 (1.09 to 0.18)
PROMIS anxietyc
.091.72 (324.81)0.52 (1.11 to 0.07)
PROMIS depressiond
.012.61 (324.73)1.15 (2.02 to 0.28)
PROMIS fatiguee
.022.39 (322.65)0.85 (1.55 to 0.15)
PROMIS sleepf
.151.45 (314.63)0.43 (0.15 to 1.01)
PROMIS socialg
.74.34 (322.51)0.14 (0.94 to 0.66)
PROMIS painh
.032.16 (307.58)1.13 (0.10 to 2.15)
FACT-Gi
<.0014.46 (300.46)1.11 (0.62 to 1.59)
FMIj
.131.52 (180.05)0.68 (1.56 to .20)
FoPk
High distressl(n=62)
<.0016.64 (200.45)0.81 (1.05 to 0.57)Distress
High anxietym(n=35)
.0013.47 (81.69)1.13 (1.77 to 0.48)HADS anxiety
High depressionn(n=20)
.032.23 (47.99)0.87 (1.65 to 0.09)HADS depression
aHADS: Hospital Anxiety Depression Scale.
bPROMIS physfunct: Patient-Reported Outcomes Measurement Information System Physical Function.
cPROMIS anxiety: Patient-Reported Outcomes Measurement Information System Anxiety.
dPROMIS depression: Patient-Reported Outcomes Measurement Information System Depression.
ePROMIS fatigue: Patient-Reported Outcomes Measurement Information System Fatigue.
fPROMIS sleep: Patient-Reported Outcomes Measurement Information System Sleep Disturbance.
gPROMIS social: Patient-Reported Outcomes Measurement Information System Ability to Participate in Social Roles and Activities.
hPROMIS pain: Patient-Reported Outcomes Measurement Information System Pain Interference.
iFACT-G: Functional Assessment of Cancer Therapy—General.
jFMI: Freiburg Mindfulness Inventory.
kFoP: Fear of Progression.
lDistress Thermometer score 5.
mHADS anxiety score 8.
nHADS depression score 8.
Adoption
According to our definition, 25% (25/100) of all enrolled
patients used the app continuously (ie, at least one completed
exercise per week) at week 20 of the intervention. The average
number (median) of completed exercises during the 20-week
intervention for all patients as well as continuous app users is
presented in Figure 2. Across all patients, the median of
completed exercises was 2 during the first week and dropped
to 0 at week 9. For continuous app users, who completed an
app exercise at least once per week until week 20, the median
of completed exercises at week 1 was 6. For the subsequent
weeks up to week 20, the median of completed exercises varied
between a median of 3 and 5 for the continuous app users.
The percentage of completed exercises is presented in Figure
3. All patients together completed 3526 exercises. Mindfulness
meditation was used most often, with a total of 1633 completed
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exercises (46.31%), followed by guided imagery with 1077
completed exercises (30.55%). Progressive muscle relaxation
was used least frequently, with 816 completed exercises
(23.14%). In both mindfulness meditation and guided imagery,
the female narrator voice was preferred.
Furthermore, female patients showed a preference for exercises
with a female narrator (1935 completed exercises with a female
narrator vs 1031 completed exercises with a male narrator).
However, male patients preferred exercises with a male narrator
(389 completed exercises with a male narrator vs 171 completed
exercises with a female narrator). The probability of choosing
the same sex in audio files is therefore increased for women by
87% and for men by 127%, which corresponds to a 2-fold higher
preference for the same sex as the narrator.
Figure 2. Completed app exercises by all enrolled patients (N=100) and by continuous app users (n=25) per week (median).
Figure 3. Completed exercises (3526) of all patients (N=100) over 20 weeks by type (mindfulness meditation, guided imagery, and progressive muscle
relaxation), gender of patient (male and female), and sex of narrator (male and female). Percentages refer to the total number of exercises per gender.
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Implementation
A total of 5 health professionals took part in an interview: 2
female nursing experts (one from an inpatient unit and the other
from an outpatient oncology unit), 2 female MBM psychologists,
and 1 male oncologist. Interviews were conducted between
January and March 2018 and lasted for an average of 45 minutes
(SD 9.54). The qualitative analysis of the interviews yielded 4
themes: (1) general impression of the app, (2) suggestions for
improvement, (3) implementation in standard care, and (4)
experience with recommending the app to patients.
Overall, the general impression of the app was positive. For
instance, the oncologist summarized his impression of the app
as follows:
I think [the app] is a very helpful thing because it is
relatively easy [to use]. You can test it. You can try
it and if you like it, you can integrate it relatively easy
into everyday life. I think it is very practical. It is a
practical thing and if patients are interested, I also
see that they take it up willingly.
All health professionals perceived the app as appealing, clearly
structured, and as a helpful supportive tool. In addition, the
MBM psychologists liked the app as an addition to the 10-week
face-to-face MBM course and appreciated the app as a good
self-help tool complementing the course. The oncologist also
stated that many patients with cancer look for something they
can use to add to standard care and an app can provide a low
threshold aid. As a negative aspect, a nursing expert stated that
a smartphone is required and not every patient possesses such
a device.
All health experts made various suggestions for improving the
app. A shared opinion was that the content of the app (ie,
number and variety of exercises) could be increased, as over an
extended period, patients might get bored with a choice limited
to 3 exercises. A nursing expert suggested that a new exercise
could, for instance, be unlocked after completing the same
exercise several times. An MBM psychologist suggested that
every week, a different selection of exercises could be activated
with alternating topics such as meditation, relaxation,
self-compassion, or body exercises. In addition, the inclusion
of exercises with different degrees of complexity was suggested.
An MBM psychologist stated that exercises for beginners (eg,
more detailed instructions, fewer moments of silence) as well
as exercises for patients experienced in mindfulness and
relaxation could be added. MBM psychologists and nursing
experts also recommended that some exercises should be
accompanied by soothing background music because longer
periods of silence might be uncomfortable for some patients.
They also recommended exercises with various lengths of time
so that patients had more flexibility if they were facing time
constraints or if they were too impatient for longer exercises.
The oncologist mentioned that adding exercises specifically for
sleep disorders might be a good addition to the app, especially
for inpatients, because poor sleep in hospitals is very common.
As an additional topic that could be added, he mentioned body
exercises such as yoga. An MBM psychologist mentioned that
an app mirroring the MBM course more closely would be great:
If I could make a wish, then I would say, it would be
totally cool to have an accompanying Mind Body
Medicine app. That is to say that a lot of
exercises—not all of them—but a lot of exercises we
do [could be added to the app]. Possibly also guided
body exercises. That would be totally cool.
The interviewees mentioned several factors that could influence
the implementation of a mindfulness- and relaxation-based app
into standard care. Both nursing experts and one of the MBM
psychologists stated that the time point when the information
of the app is delivered to the patient might be important. These
health professionals mentioned that the patients were bombarded
with information during the first consultation or during the first
day when a patient enters the hospital and additional information
about the app might overwhelm some patients. The outpatient
nursing expert also mentioned that they are often limited because
of time constraints during consultation hours:
On the one hand there are the concerns of the
patients, which you have to discuss. But you also have
a little bit of pressure, [to tell them] all relevant
information. [...] And sometimes it’s already two
minutes before the end [of the consultation]. [...] And
you can’t just hand out the flyer. You also need to say
a few words [about the app] and that’s why I
sometimes forgot [to mention the app]. Due to
shortage of time.
The nursing experts also mentioned that the nurses oftentimes
forgot about the app because it is not part of standard care.
Therefore, the nursing experts stated that it might be helpful to
better inform the nurses about the app and setting up standards
regarding the communication about the app, for example, when
to inform the patients and how. In addition, the nursing experts
stated that it might be helpful if they had a demonstration device
at the oncology unit so that they could better explain the app to
the patients. All interviewed health professionals further
mentioned that patients with cancer are very diverse and that
although some patients are very eager to try out various
treatments, others are not. One MBM therapist also stated that
not all patients perceive relaxation as important and that those
patients might need some additional information which indicates
why relaxation is good for them. All health professionals also
stated that implementing such an app does not result in a lot of
additional work for them and they appreciate the app, which
they could recommend to suitable patients.
Regarding their experience with recommending the app to
patients, health professionals shared the opinion that female
patients are more drawn to mindfulness and relaxation exercises.
Furthermore, the MBM therapists stated that patients who
already practiced some form of relaxation or meditation often
did not participate in the study. The MBM therapists also noticed
that the composition of the MBM group had an influence on
how many patients were willing to try out the app. For instance,
if one patient was very motivated and expressed interest in the
app, hesitant patients sometimes followed suit and were willing
to try the app as well. One MBM therapist also noticed that
many older people were willing to use the app:
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I was surprised that so many older patients had the
app on their phone and also used the app regularly
[...]. I had the impression, that it appeals to the young.
[...]. But oftentimes, the older people have more time,
because they don’t work anymore.
Discussion
Principal Findings
In this study, we explored the feasibility of a mindfulness- and
relaxation-based self-help app for patients with cancer. To
evaluate the feasibility, we used the RE-AIM framework [37],
and in this analysis, we focused on the framework dimensions
effectiveness, adoption, and implementation. Our findings
support the feasibility of this mHealth intervention. The results
indicate that the intervention might have beneficial effects on
patients’distress and quality of life. Furthermore, the mHealth
intervention is accepted by the target population as well as by
health professionals.
For the dimension effectiveness, we looked into pre-post effects.
Our results suggest that the app might have the potential to
reduce distress, fatigue, sleep disturbance, and anxiety as well
as improve health-related quality of life and mindfulness. This
is in line with a recent pilot study [47], in which a mobile
mindfulness-based stress reduction program improved, among
others, stress, anxiety, depression, sleep quality, quality of life,
and mindfulness in patients with breast cancer with small to
large effects. Furthermore, a recent randomized controlled trial
conducted by Kubo et al [48] assessed the feasibility of a
commercially available mindfulness program in which they
targeted patients with cancer and their caregivers. This program
leads to an increase in quality of life in patients with cancer
with a medium effect size [48]. Similar to these findings, Rosen
et al [49] reported that the quality of life of patients with breast
cancer improved with a small effect size using a commercially
available mindfulness course when compared with a control
group.
As depressive symptoms and anxiety were not significantly
reduced in the total sample in our study, we also looked at
subsamples with higher HADS scores. In the high anxiety and
high depression subsamples, anxiety and depression,
respectively, decreased significantly over time. This might
indicate that a mindfulness and relaxation mHealth intervention
is especially beneficial for patients with cancer with higher
emotional distress. This is also in line with a study by Barth et
al [50], where highly distressed patients benefited most from
psycho-oncological interventions. However, we did not find
any group effects when comparing continuous app users with
intervention dropouts. This might indicate that our definition
of users and dropouts is not precise enough or that another
variable than time spent practicing is responsible for changes
in outcomes.
For adoption, our results showed that at week 20 of the
intervention, 25 of 100 patients were using the app continuously.
With 54 of 100 continuous app users at week 10 [36], this leads
to a dropout rate of approximately 50% every 10 weeks. The
25 continuous app users practiced on average 3 to 5 times per
week (median), which comes close to our initially stated
recommendation of 5 exercises per week. We consider this a
good adoption of the mHealth intervention because the
intervention was set up as a self-care intervention without the
involvement of a therapist or health professional. Mindfulness
was the preferred exercise, followed by guided imagery and
progressive muscle relaxation. However, mindfulness meditation
exercises were also presented as the first choice in the app,
whereas guided imagery was placed at the second position, and
progressive muscle relaxation was placed at the third position.
Therefore, the preference for mindfulness meditation could also
be caused by the placement of the exercises in the app. These
results regarding adoption are comparable with those of a study
conducted by Kubo et al [48], in which patients with cancer
received access to the commercially available mindfulness app
Headspace (TM). In this study, 40 of 54 patients with cancer
allocated to the intervention group completed the 8-week study,
and 20 patients with cancer used the app on at least 50% of the
days [48].
The results from the interviews with health professionals provide
some insights into the implementation of a mindfulness and
relaxation mHealth intervention into standard care. In general,
all interviewed health professionals perceived the app as a
helpful addition to standard care. The health professionals also
suggested some improvements, which might increase the
acceptance and long-term use of such mHealth interventions
by patients. A suggested improvement shared by all health
professionals is the increase in the content of the app, such as
additional exercises or variations of the exercises. A statement
about the implementation of the mHealth intervention given by
several health professionals was the adequate provision of
information. One of the interviewed MBM psychologists as
well as the nursing experts stated that patients with cancer are,
on the one hand, flooded with information, especially when
they start their treatment. However, the provision of some
information to the patients about a mHealth intervention is
necessary, at least to let the patients know about the existing
intervention. On the other hand, nursing experts also mentioned
that nurses often forgot about the intervention, although they
approve this kind of intervention. Therefore, a standardized
procedure for informing patients about the mHealth intervention
might facilitate the implementation of the intervention. In
addition, health professionals such as nurses might have to be
informed regularly about such interventions because it is not
part of their standard treatment; therefore, they might forget
about it, as seen in this study. Regarding the recruitment process,
the health professionals made the observation that female
patients were more interested in this mHealth intervention. This
is also reflected by the gender ratio in this study’s sample, with
76 female and 24 male patients with cancer, which is typical
for complementary and alternative treatments [51-53]. This
gender difference raises the question of whether an effort should
be made to better recruit male patients with cancer for such an
intervention. A nursing expert, for instance, mentioned during
the interview that a focus on more technical aspects or facts
could be more appealing to male patients.
Strengths, Limitations, and Future Directions
This study has several strengths and limitations. A strength of
the study is the collection of objective data in the form of
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logging the exercise use for each patient over the course of 20
weeks. Therefore, data on using the app exercises were not
biased through self-report. Another advantage of this study was
the use of a mixed methods approach, which is recommended
for the development of digital interventions [54].
A limitation of the study is that we did not have a control group.
Therefore, the effectiveness of the app cannot be determined in
this study because regression to the mean could have an impact
on the improvement of well-being. Furthermore, we used
paper-and-pencil questionnaires, which might have led to more
missing data compared with web-based questionnaires [55].
However, this was compensated by using LMM analyses, which
take into account all patients who provided baseline data.
Another limitation is that we did not assess whether patients
were practicing mindfulness and relaxation exercises without
the app, which could have an effect on the assessed outcomes.
Therefore, future studies should investigate this topic with a
randomized controlled trial to determine the effectiveness of a
mindfulness and relaxation mHealth intervention. Our study
provides some insights regarding the effects that might be
expected in a similar study, which will be helpful to power
future studies sufficiently. We also looked at aspects of
implementing an mHealth intervention. All interviewed health
professionals perceived such an mHealth intervention as a
helpful addition to standard care, but as described earlier, they
also stated barriers to the implementation of such an
intervention, which should be investigated in future studies.
Future studies could also investigate an mHealth intervention
with more content than in this study app, as suggested during
the interviews by health professionals. For instance, audio files
with background music or exercises with variations in their
duration could be added. In addition to mindfulness and
relaxation exercises, physical exercise programs could be added.
Physical exercise can have beneficial effects on symptoms of
patients with cancer [56], and physical exercise has already
been implemented in mHealth apps for patients with cancer
[57].
Conclusions
The results of this observational feasibility study indicate that
a mindfulness and relaxation app can be a feasible and an
effective way to deliver a self-care intervention for patients with
cancer. Our results indicate that such an intervention might be
especially beneficial for highly distressed patients with cancer.
The appeal of such an app could be increased with more diverse
content, which might also positively affect the adherence of
patients to such an intervention. The effectiveness and further
aspects regarding the implementation of such an mHealth
intervention should be investigated in a future randomized
controlled trial.
Acknowledgments
This study was funded by the Swiss Cancer League (KLS-3564-02-2015).
Conflicts of Interest
None declared.
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Abbreviations
FACT-G: Functional Assessment of Cancer Therapy–General
FMI: Freiburg Mindfulness Inventory
FoP-Q-SF: Fear of Progression Questionnaire-Short Form
HADS: Hospital Anxiety and Depression Scale
LMM: linear mixed model
MBM: mind-body medicine
mHealth: mobile health
PROMIS-29: 29-item Patient-Reported Outcomes Measurement Information System
RE-AIM: Reach, Effectiveness, Adoption, Implementation, and Maintenance
Edited by G Eysenbach; submitted 27.10.19; peer-reviewed by E Børøsund, K Rosen; comments to author 20.01.20; revised version
received 13.03.20; accepted 18.11.20; published 13.01.21
Please cite as:
Mikolasek M, Witt CM, Barth J
Effects and Implementation of a Mindfulness and Relaxation App for Patients With Cancer: Mixed Methods Feasibility Study
JMIR Cancer 2021;7(1):e16785
URL: https://cancer.jmir.org/2021/1/e16785
doi: 10.2196/16785
PMID: 33439132
©Michael Mikolasek, Claudia Margitta Witt, Jürgen Barth. Originally published in JMIR Cancer (http://cancer.jmir.org),
13.01.2021. 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, and reproduction in any medium,
provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to
the original publication on http://cancer.jmir.org/, as well as this copyright and license information must be included.
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... Why do these symptomatic women not look for a health provider's help? Limited access or a lack of awareness might be the main cause for this gap [14]. ...
... To overcome these barriers, providing health education to both health providers and patients may be a promising way to deliver respective supportive treatments [14]. In fact, symptom-awareness model campaigns have increasingly formed part of global disease (cancer, menopause, fatigue, etc.) control strategies [15][16][17], because lack of awareness is the most critical barrier for both health providers and patients [14]. ...
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Objectives: While patients with head and neck cancer (HNC) are known to experience higher levels of anxiety and depression, they do not always use psychosocial oncology (PSO) services when available. This study aimed to investigate barriers to PSO service utilization in this patient population, with the goal of appropriately targeting outreach interventions. Methods: A conceptual model based on the Behavioral Model of Health Services Use was tested in 84 patients newly diagnosed with a first occurrence of HNC followed longitudinally over one year, including variables collected through self-administered questionnaires, Structured Clinical Interviews for DSM (SCID-I), and medical chart reviews. Results: Within the first-year post-diagnosis, 42.9% of HNC patients experienced clinical levels of psychological distress, with only 50% of these consulting PSO services (29% total). A logistic regression indicated that PSO utilization was increased when patients presented with advanced cancer(p=0.04) and a SCID-I diagnosis of major depressive disorder, anxiety disorder, or substance use disorder(p=0.02), while there was an inverse relationship with self-stigma of seeking help(p=0.03); these variables together successfully predicted 76.3% of overall PSO utilization, including 90.6% of non-users. Conclusions: Future outreach interventions in patients with HNC could address stigma in an attempt to enhance PSO integration into routine clinical care.
Article
Objective The primary objective was to evaluate the efficacy of commercially available mobile app‐delivered mindfulness training (AMT), compared to waitlist control (WC), on quality of life (QOL) among women diagnosed with breast cancer. The secondary outcome was dispositional mindfulness. Enrollment, app utilization, and study completion are reported as feasibility objectives. Methods Women diagnosed with breast cancer ≤ 5 years (n=112) were randomized to AMT (n=57) or WC (n=55), over 8‐weeks, with 4 weeks of follow‐up. We conducted linear mixed effects models to examine group by observation interactions on QOL and dispositional mindfulness at baseline, during intervention (5‐weeks), post‐intervention (9‐weeks), and follow‐up (12‐weeks post‐baseline). Results Participants assigned to AMT reported higher QOL, compared to those assigned to WC, from baseline through follow‐up t(258.40)=3.09, p < 0.01, 95% CI [2.71, 11.90]. Participants assigned to AMT also reported higher dispositional mindfulness, compared to those assigned to WC, from baseline through follow‐up t(268.44)=2.04, p=0.04, 95% CI [0.01, 0.57]. App utilization data was obtained from 34 participants. Fewer participants assigned to AMT completed all study assessments, compared to participants assigned to WC, (χ2 (1)=7.07, p=0.008). Conclusions Findings suggest commercially available AMT may proffer some benefit to women seeking to enhance their QOL following breast cancer diagnosis.