JMIR FORMATIVE RESEARCH

Gould et al

Original Paper

Effects of Mobile App–Based Intervention for Depression in
Middle-Aged and Older Adults: Mixed Methods Feasibility Study
Christine E Gould1,2, PhD; Chalise Carlson1, MA; Flora Ma1,3, PhD; Valerie Forman-Hoffman4, MPH, PhD; Kristian
Ranta4, MSc; Eric Kuhn2,5, PhD
1

Geriatric Research, Education, and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, United States

2

Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States

3

Pacific Graduate School of Psychology, Palo Alto University, Palo Alto, CA, United States

4

Meru Health, Inc, San Mateo, CA, United States

5

National Center for Posttraumatic Stress Disorder, VA Palo Alto Health Care System, Menlo Park, CA, United States

Corresponding Author:
Christine E Gould, PhD
Geriatric Research, Education, and Clinical Center
VA Palo Alto Health Care System
3801 Miranda Ave
Palo Alto, CA, 94304
United States
Phone: 1 6504935000 ext 68899
Email: cegould@stanford.edu

Abstract
Background: Digital mental health interventions may help middle-aged and older adults with depression overcome barriers toaccessing traditional care, but few studies have investigated their use in this population.
Objective: This pilot study examines the feasibility, acceptability, and potential efficacy of the Meru Health Program, an 8-week
mobile app–delivered intervention.
Methods: A total of 20 community-dwelling middle-aged and older adults (age: mean 61.7 years, SD 11.3) with elevateddepressive symptoms participated in a single-arm pilot study investigating the Meru Health Program, an app-delivered intervention
supported by remote therapists. The program primarily uses mindfulness and cognitive behavioral skills to target depressive
symptoms. A semistructured interview was completed at the baseline to establish current psychiatric diagnoses. Depressive
symptoms were measured using the Patient Health Questionnaire and Patient-Reported Outcomes Measurement Information
System (PROMIS) depression measures. Anxiety symptoms were measured using the Generalized Anxiety Disorder Scale and
the PROMIS Anxiety measure. User experience and acceptability were examined through surveys and qualitative interviews.
Results: In total, 90% (18/20) of the participants completed the program, with 75% (15/20) completing at least 7 of the 8
introductory weekly lessons. On average, participants completed 60 minutes of practice and exchanged 5 messages with their
therapists every week. The app was rated as helpful by 89% (17/19) participants. Significant decreases in depressive (P=.03) andanxiety symptom measures (P=.01) were found; 45% (9/20) of participants showed clinically significant improvement in either
depressive symptoms or anxiety symptoms.
Conclusions: The findings suggest that the commercially available Meru Health Program may be feasible, acceptable, andpotentially beneficial to middle-aged and older adults. Although larger controlled trials are needed to demonstrate efficacy, thesefindings suggest that digital health interventions may benefit adults of all ages.
(JMIR Form Res 2021;5(6):e25808) doi: 10.2196/25808
KEYWORDS
aging; depression; digital health; digital therapeutics; mHealth; mobile phone

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Introduction
Background
Major depressive disorder (MDD) is among the foremost causesof disability worldwide among older adults [1-3]. In adults aged
55 years and older in the United States, 5.6% experienced MDD
within a 1-year period [4]. Two nonpharmacologicalinterventions, cognitive behavioral therapy (CBT) and
mindfulness-based interventions have been efficacious in
reducing late-life depressive symptoms [5-7]. However, highrates of depression persist because of undertreatment [8,9].
Older adults face multiple barriers in accessing mental healthinterventions, including limited transportation and mobility,
living distally from treatment settings, and high health carecosts, which could be addressed with digital health interventions[10,11]. As smartphone use grows, interest in using mobile apps
to deliver therapeutic interventions has proliferated [12]. A
meta-analysis [13] of randomized controlled trials of mobile
apps for depression in adults found a moderate effect size thanin inactive controls and a small effect size than in active
controls. The applicability of these findings to older adults is
unknown because the majority (65%) of randomized controlled
trials in the meta-analysis included younger participants (age:mean ≤30 years). Trends in smartphone ownership suggest thatolder adults, particularly baby boomers, are rapidly adopting
mobile devices [14], yet many older adults have not yet usedmental health–related mobile apps, despite reported interest andopenness to these interventions [15,16].
Few studies have examined mental health–related mobile apps
among older users. One group tested an adapted app-based
intervention to promote the self-management of chronic
conditions alongside peer coaching among middle-aged andolder adults with serious mental illness and found it to befeasible, acceptable, and potentially helpful in improving
outcomes among the 8 study completers [17]. Another small
study examining an app targeting mental wellness in agroup-based treatment setting yielded positive feedback about
the app from older users, but the findings were limited bysubstantial attrition [18]. These small studies suggest theacceptability of mental health apps among older users whenused alongside in-person support (ie, peer coaches andgroup-based support). Given the barriers to care faced by older
adults, there is a need to examine app-based interventions that
do not include extensive in-person support.

Objectives
This study investigates the use of an 8-week mobile app–based
intervention with remote therapist support called the Meru
Health Program (version 2.0), a commercially available digital
health program. The Meru app contains video instructional
content and guided practices delivered to a group of 5 to 10
individuals who work through the program as a cohort. Videos
and practices teach CBT and mindfulness techniques to manage
depressive and related symptoms, such as worry. Patients haveaccess to a dedicated therapist via asynchronous within-app
messaging or, if needed, a phone call. The therapist provides
guidance by (1) responding to patients’ messages, (2) sending
unprompted messages to patients and encouraging program
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Gould et aladherence, and (3) reviewing responses to practice exercises.
The app also includes a moderated discussion where groupmembers can share thoughts and experiences with practices andsupport other patients by providing limited feedback on theirposts.
An uncontrolled study of the Meru Health Program in young
adults demonstrated feasibility and reduced depressive
symptoms [19]. Similarly, a second uncontrolled study foundthat 60% exhibited clinically significant improvement indepressive symptoms at the 12-month follow-up [20]. This study
extends to an understudied age group of middle-aged and older
adults. It examined the feasibility, acceptability, and preliminary
efficacy of the Meru Health Program among middle-aged andolder adults using a mixed methods approach.

Methods
ParticipantsParticipants were recruited through various announcements
describing a mobile app intervention for depression using (1)flyers posted at medical offices and on community boards (eg,at libraries and coffee shops), (2) web-based advertisements(Craigslist and Facebook), (3) word of mouth, and (4) invitations
to previous research participants interested in future research
opportunities.
The inclusion and exclusion criteria were assessed during thetelephone screening. Inclusion criteria were age ≥40 years,
elevated depressive symptoms, owning a smartphone, andresiding in California because of telehealth laws regarding
practicing across state lines. Exclusion criteria included
cognitive impairment, suicidal ideation, current participationin psychotherapy, problematic drinking, and psychotic
symptoms. Participants taking psychotropic medications musthave been on a stable dose (>1 month) to be included.

Measures
Demographic and Health Questionnaire
Demographic information and health status, presence of eight
major health conditions (arthritis, asthma or bronchitis, cancer,
diabetes, epilepsy, heart disease, hypertension, and stroke), andvision and hearing impairment were gathered at baseline via
questionnaires.

The Mobile Device Proficiency Questionnaire
The Mobile Device Proficiency Questionnaire (MDPQ) [21]examines experience with and aptitude for using mobile devices
across eight domains: mobile device basics, communication,
data and file storage, internet, calendar, entertainment, privacy,
troubleshooting, and software management. Each of the 46
items is rated using a 5-point scale with an option for “nevertried” (1) and then ratings for ease of completion from “not atall” (2) to “very easily” (5). The total score ranges from 8 to40, with higher scores reflecting greater proficiency. Internalconsistency was excellent (Cronbach α=.96). The MDPQ wascompleted at the baseline.

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Patient Health Questionnaire 9-Item
The Patient Health Questionnaire 9-item (PHQ-9) [22] measures
the frequency of depression symptoms over the last 2 weeks.
Total scores range from 0 to 27, with higher scores reflecting
increased depressive symptom severity. Internal consistency
varied across timepoints (baseline α=.60; week 5 α=.82;
posttreatment α=.77). The PHQ-9 was completed at the
telephone screen, baseline, week 5, and week 8 (posttreatment).
Within the app, the PHQ-9 was completed during weeks 1, 3,
5, 7, and 8.

Generalized Anxiety Disorder 7-Item Scale
The Generalized Anxiety Disorder 7-item (GAD-7) scale [23]measures anxiety symptoms characterizing Generalized Anxiety
Disorder. Total scores range from 0 to 21, with higher scoresreflecting increased anxiety symptom severity. Internalconsistency was acceptable to good across timepoints (baseline
α=.77; week 5 α=.83; posttreatment α=.86). The GAD-7 wascompleted at baseline, week 5, and posttreatment. Within theapp, the GAD-7 was completed during weeks 1, 5, and 8.

Patient-Reported Outcomes Measurement Information
System Depression and Anxiety Measures
The Patient-Reported Outcomes Measurement Information
System (PROMIS) Depression (short form 8a) and Anxiety(short form 8) scales were used as secondary measures [24].
Total scores ranged from 8 to 40 on the PROMIS Depression
and from 7 to 35 on the PROMIS Anxiety, with higher scoresreflecting increased symptom severity. The PROMIS Depression
and Anxiety scores had good-to-excellent internal consistency
at baseline (α=.90 and .89, respectively) and posttreatment(α=.97 and .95, respectively). The PROMIS measures werecompleted at baseline and at weeks 5 and 8.

Mini International Neuropsychiatric Interview
The Mini International Neuropsychiatric Interview (MINI 7.0.2)[25] is a semistructured interview that assesses psychiatricdisorders using the Diagnostic and Statistical Manual of Mental
Disorders 5th Edition [26] criteria. The MINI psychotic screen
was administered during the telephone screen; the remainder
was administered at baseline to assess current major depression
and other comorbid psychiatric disorders. For diagnostic
accuracy and supervision purposes, the principal investigator
conducted live or audio-recorded observations for half of theinterviews conducted by the research staff.

App UseThe app use data collected included the number of messages
sent, duration of use, lessons completed, and the number andtypes of practices completed.

User Experience Survey
At posttreatment, participants completed a 6-item self-reportmeasure about their experiences and impressions of the program(Multimedia Appendix 1). Participants ranked the four
components of the app (information, practices, therapist chat,and group support) from most to least helpful. Participants thenresponded to five statements about the ease of use of the app,
helpfulness of the app, helpfulness of the emails, frequency ofhttps://formative.jmir.org/2021/6/e25808

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Gould et al
the emails, and duration of the program using a 5-point
Likert-type scale ranging from 1 (strongly disagree) to 5
(strongly agree).

Qualitative Interview
After treatment, participants completed a semistructured
qualitative interview about the program (Multimedia Appendix
1). They were asked about helpful and unhelpful aspects of the
program, technical or navigational issues, program benefits,
and suggested improvements. The interviews wereaudio-recorded, transcribed, and reviewed for transcription
accuracy.

Procedures
Initial Screening and Baseline Assessment
The study was approved by the Stanford University School of
Medicine Institutional Review Board and registered at
ClinicalTrials.gov (NCT03652948). The study was conducted
between July 2018 and May 2019.
Potential participants completed a telephone screen to assess
the inclusion and exclusion criteria. The PHQ-9 [22] determined
the presence of elevated depressive symptoms (≥10) [27]. If
PHQ-9 item 9 (death or suicide ideation) was endorsed asseveral days or more, then the P4 Suicide Risk Screener [28]was administered to assess active suicidal ideation and, if
present, individuals were excluded and referred for treatment.
Questions about current mental health treatment assessed for
current psychotherapy and use of medications. The Short
Blessed Test [29] identified possible cognitive impairments(≥6). The Alcohol Use Disorders Identification Test (AUDIT-C)[30] was used to identify problematic drinking (≥5). Finally,
possible psychotic disorders were identified using a psychotic
screen from the MINI [25].
Eligible participants were invited to an in-person visit, during
which the research staff obtained informed consent, followedby administration of the complete MINI and self-reportassessments. Participants then received a handout explaining
the steps to begin the Meru Health Program. First, study referrals
were sent directly to the Meru therapist, who scheduled an
introductory call with the participant. Five days before the startof the program, participants received an email with instructions
on downloading and logging into the app on their own
smartphone.

Meru Health Program
Meru Health is a registered health care provider in multiple
states in the United States and Finland. Individuals primarily
access the Meru Health Program by selecting health andemployee plans. The app is available for download via the
App Store (iOS) or Play Store (Android), but patients requirea referral and eligibility screen to access the app after
download. As described earlier, the program consists of an app,therapist support (via app and weekly emails), and group
discussion. The app has five menu options: (1) “today screen”
with informational videos and practices to be completed, (2)group discussion page, (3) program timeline, (4) notifications,and (5) asynchronous within-app messaging with the therapist(Figure 1). Participants can review previous days’ content
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through the program timeline screen and are advised of missed
content through notifications. The interactive aspects of the
program include the group-based discussion feature wherein

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participants can share their thoughts, and other members can
respond with either a heart icon or one of four supportive
statements.

Figure 1. Meru Health Program version 2.0 screenshots.

Meru Health therapists are licensed practitioners with doctoralor master’s degrees in psychology, counseling, social work,
marriage and family therapy, or other related fields. Therapists
have formal training in mindfulness, CBT, and manual training(>30 hours), which includes an assessment of skill acquisition
to deliver the Meru Health Program. Therapists send weekly
emails introducing new content (ie, CBT or mindfulness skills)
to be learned within the app each week. Therapists monitor
practice completion; send messages to encourage adherence;and review participants’ messages and group discussion posts
for possible suicide or homicidal ideation, active substance use,or the presence of psychotic symptoms. The therapist responded
to participants exhibiting these symptoms or those who had
evidence of worsening mental health within 12 hours. In
addition, posts deemed inappropriate or potentially harmful toother group members (ie, triggering) were removed.

5- and 8-Week (Posttreatment) Assessments
Participants were contacted by researchers during week 5 tocomplete questionnaires assessing depressive and anxiety
symptoms. At the posttreatment assessment, participants
completed these questionnaires again and provided feedback
about the program via a user experience survey and qualitativeinterview. Participants received payment only for completionof assessments administered by the research team at baseline,
week 5, and week 8/posttreatment (total compensation=US $80;
US $40 for baseline and posttreatment assessment). No otherpayment was provided.

Statistical Analysis
To assess the feasibility of the program, we summarizedparticipant flow, inspected retention rates, and calculated themean use of different app components using the app use data.
Acceptability was examined by summarizing responses to theuser experience survey.

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The qualitative interview transcripts were excerpted by the firstauthor (CEG) using Dedoose 8.2.14 (SocioCultural ResearchConsultants) web-based software [31]. A codebook wasdeveloped using inductive (ie, review of transcripts) anddeductive methods. Excerpts were independently coded by the
2 coders. The codebook was revised and adjudicated three times
to reach a prespecified reliability of κ>0.70 (final pooledκ=0.81). Excerpts were discussed among 3 authors (CEG, CC,
and FM), and matrices were created to examine variations inpatterns or themes by quantitative ratings consistent with amixed methods approach [32].
Intervention effects on depressive and anxiety symptoms were
estimated using linear mixed effects models using SPSS version
24 (IBM) [33]. All participants were included in the analyses,
which used intention-to-treat principles. The models included
random intercepts and slopes and used residual maximum
likelihood estimation owing to the small sample size [34]; 5%of the data points on dependent variables were missing becauseof dropouts. Improvement in symptoms indicative of response
was defined as having equal to or surpassing the minimallyclinically important difference of five points or more [35] on
the PHQ-9 or 4 points or more [36] on the GAD-7.

Data Availability
A limited data set that supports the findings of this study isavailable from the corresponding author upon reasonable
request.

Results
Participant Characteristics
Participants (N=20) had a mean age of 61.65 years (SD 11.32;
range 42-81 years), and most were women (14/20, 70%) andWhite and non-Hispanic (12/20, 60%; Table 1). In total, 60%

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JMIR FORMATIVE RESEARCH(12/20) participants were aged 60 years or older; 70% (14/20)

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met the criteria for the current MDD.

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Table 1. Participant characteristics (N=20).
Participant characteristics

ValuesAge (years), mean (SD)61.65 (11.32)

Education (years), mean (SD)16.60 (2.46)

Sex, n (%)
Female14 (70)Male

6 (30)

Race and ethnicity, n (%)
White, non-Hispanic

12 (60)

Any race, Hispanic

2 (10)

Asian

3 (15)

Multiracial

3 (15)

Marital status, n (%)
Single10 (50)Married3 (15)

Separated or divorced

6 (30)

Widowed

1 (5)Current living situationa, n (%)
Alone

9 (45)

With spouse or partner

7 (35)

With relative or roommate

3 (15)

Employment, n (%)
Full-time

6 (30)

Part-time

2 (10)

Unemployed

7 (35)

Retired5 (25)Taking psychotropic medications, n (%)

10 (50)Self-rated health, n (%)
Excellent

1 (5)

Good

11 (55)Fair

6 (30)Poor

2 (10)

Current medical conditions, n (%)
Arthritis

10 (50)Asthma or bronchitis

7 (35)

Cancer1 (5)Diabetes

2 (10)

Heart disease

2 (10)

Hypertension

7 (35)

Sensory difficulties, n (%)
Use eyeglasses

18 (90)Hearing loss (both ears)

7 (35)

Use hearing aids

3 (15)

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Participant characteristics

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ValuesPrevalence of current psychiatric diagnoses (MINIb), n (%)
Major depressive disorder

14 (70)

Anxiety disorders

12 (60)

Posttraumatic stress disorder

3 (15)

Other disordersc

5 (20)

MDPQd, mean (SD)34.68 (4.62)aN=19. Percentages of medical conditions, sensory difficulties, and psychiatric diagnoses do not add up to 100% because participants may have had
more than 1 condition.
b

MINI: Mini International Neuropsychiatric Interview.

cOther disorders include alcohol use (early remission), obsessive-compulsive disorder, and binge eating disorder.

d

MDPQ: Mobile Device Proficiency Questionnaire.

Feasibility, User Experience, and Acceptability
As displayed in Figure 2, 90% (18/20) participants completed
the program, and 2 discontinued the program. One individual(aged >60 years) discontinued the program because of technicalchallenges with using the app on an older smartphone but
completed the postassessment; the other (aged <60 years)
discontinued the program because of spontaneous symptom
improvement and activities conflicting with app use and did notcomplete the postassessment. Of the 4 groups conducted during
the study, 3 included some Meru Health Program nonstudy
patients; one group (n=5) included only study participants. The
groups’ sizes ranged from 5 to 9 participants. In the mixed
groups of study and nonstudy participants, the nonstudy patients

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comprised half (3/6, 50%) of the participants in one group or asmaller minority (1/9, 11% and 2/6, 33%) in the other twogroups. Each group was led by 1 of 2 therapists. No qualitative
differences emerged based on which therapist led the groups orwhether the groups consisted of study participants only or study
and nonstudy participants. Per the app use data (N=20), allparticipants downloaded the app, logged on, messaged with
their therapist, and practiced at least once. The mean durationof time spent on practices throughout the program was 9.07
hours (SD 6.67), which averaged out to approximately 68
minutes each week. Participants sent 13.4 (SD 10.89) messages
to their therapist, with a range spanning 2 to 38 messages. Most
(15/20, 75%) completed at least 7 of the 8 weekly introductorylessons.

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Figure 2. Flow of participants through the study. PHQ-9: Patient Health Questionnaire 9-item.

The user experience survey results displayed in Table 2 highlightthat the program was deemed usable by most with some nuances
identified during the qualitative interview. Most participants(15/19, 79%) indicated that the program somewhat or completely
met their expectations, and 89% (17/19) believed the app was
helpful. Examinations of the core program components revealed
that the practices were deemed most helpful, and group
discussion was the least helpful (Table 3). The inclusion of atherapist proved very important, as highlighted by one
participant:
Having the therapist there for me was great in thefact that I felt like it wasn’t just a computer that I was

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interacting with in the program. She provided apersonal touch which was very important in the
program. Because I think we all, at least me, like to
be validated by a human, it makes it [the program]
more real to me...I had her in case the app wasbothering me, I needed a connection to someone that
I can talk to about, or if I didn’t understandsomething, I had her to be there for me.
Another program component, the group discussion component,
was deemed the least helpful. However, some participants
appreciated reading what others posted (Table 2).

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Table 2. User experience survey and qualitative interview findings.
Item

Responses, n (%)

Qualitative interview findings

Example quotes

App usability: the Meru•

Challenges included difficulty reviewing
previous content, typing long answers in
response to questions after practices,
progress not being saved, crashing, or
freezing.

•

Health app was easy to use.a•“On some of them [practices] I could
pause and rewind and play again, butsome of them I could only pause andplay.”“I’m a typer as opposed to a tapper.
I’m much more able to ramble like I’dlike to if I had a keyboard in front ofme...I seem to have a better hand at
expressing my thoughts when I can goback and quickly edit.”Communication: the emails •from Meru Health were••
helpful to me.b••9 (45): strongly agree Some expected more communication direct- •ly with the therapist. Three were unaware
4 (20): agree
that they would be receiving emails.
4 (20): neutral
1 (5): disagree
0 (0): strongly disagree•“I occasionally got confused whether
messages would show up in my email
inbox or whether they would show up
on the app.”“[emails] would break down the topics,and I think that was fabulous, that wasvery informative. Without that information, it would have been difficult because it was not really explained, I
went back and read the emails a few
times, so it would make some sense.”

Communication frequency: •the emails from Meru Health•were frequent enough.b•11 (55): strongly agree Weekly emails were frequent enough for•most participants. Most comments reflected
6 (30): agree
a desire for more personalized communication.
0 (0): neutral“I thought there would be a little moreof the individual therapist or individual
communication between the therapist
and myself, just a little more of that, a
lot more...”Program length: the Meru
Health Program was theright length of time.a

a

•
•
•

5 (25): strongly agree;
12 (60): agree
0 (0): neutral
2 (10): disagree
0 (0): strongly disagree••

1 (5): disagree
0 (0): strongly disagree

•
•
•
•
•

9 (45): strongly agree Six participants recommended that it could •be longer (9 to 24 weeks). One recommend4 (20): agree
ed it be shorter (4 weeks).
2 (10): neutral
3 (15): disagree
1 (5): strongly disagree“I think it would be great if the app
gives [the] participant a time to choose
from, for me 3 months would havebeen ideal.”

n=19.

b

n=18.

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Table 3. Mixed methods evaluation of program components (n=19).
Component

Most helpfula, n(%)

Least helpful, n(%)

Helpful aspects

Practices

12 (63)

0 (0)

•
•
•

Areas to improve

Having narrator-guided practices•bSpecific practices, spanning CBT and
mindfulness deemed helpful•
Included 3-minute reset, breathing,
mindfulness, self-compassion, establishing boundaries, and thought record ••Therapist Support

7 (37)

2 (11)•••

Information
Provided

4 (21)

2 (11)

•
•

Group

a

0 (0)

15 (79)•Usability issues such as loss ofprogress if interrupted (or exiting app)while practicing
Desire for chimes or signals whenpractices end (too much silence)
Need for more introductory practices
for certain components (eg, self-compassion)
Clearer option to skip reflection questions after practices

Therapist was caring, thoughtful, gen- •uine, and supportive
Provided helpful feedback and com- •ments on participant entries
Helped personalize the program byproviding additional information andresources when necessary

Unclear how frequently to interact withtherapist
Unclear how much information toshare with therapist

Education and information about mood •and thinking patterns
Provides underlying rationale for CBT
and mindfulness practices

One participant requested practices tohelp differentiate rumination from reflection concerning thought boundaries.

Reading others’ responses helped•
people feel less alone and feel validated in their struggles.
••

Confusion about how to use the group
and need for guidelines
Low rates of participants using group
Limited response options (preprogrammed drop-down) to other members’ comments

Three individuals ranked more than 1 component as most helpful; 1 person did not select the most helpful component.

b

CBT: cognitive behavioral therapy.

Qualitative findings highlighted the aspects of each component
that were confusing or would benefit from improvement.
Specifically, participants wanted more guidance about how best
to use aspects of the program, ranging from questions about theinterface to the need for some guidelines in using the therapist
and group support. Regarding group support, participants
expected to have more flexibility in responding to other groupmembers:
A couple of things I read that other people [posted],
I wanted to give a comment on, but you were limited
to 4 choices...it didn’t give you the opportunity to sayanything personal, positive, in your own words.
Other suggestions pertained to having an improved orientation
to the app and guidance on how frequently they communicate
with the therapist.

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Preliminary Effects of the Program on Mental HealthSymptoms
Figure 3 displays the mean PHQ-9 and GAD-7 mean scores
across timepoints. Table 4 presents the results of the linear
mixed effects model. Time was significant (P=.03-.001) in all
4 linear mixed models, which demonstrated that both depressive
and anxiety symptoms reduced across the 8-week program(Table 4). The (uncontrolled) effect sizes ranged from medium
to large (Hedges g values=.41-.94). Regarding clinicallysignificant improvement, 30% (6/20) participants had
improvements in depressive symptoms and 45% (9/20) inanxiety symptoms. All 6 who experienced improvement indepression also showed improvement in anxiety. Age and mobile
device proficiency were not significantly associated with change
in scores on any outcome measures, but older age was associatedwith lower mobile device proficiency (r=−0.60; P=.005). Mobile
device proficiency total scores ranged from 26.47 to 40, whichfalls above the average for older adults (mean 19.2, SD 10.5)in the initial MDPQ validation paper [21].

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Figure 3. Mean depression and anxiety symptoms measured within the app and by paper or phone assessments. GAD-7: Generalized Anxiety Disorder
7-item; PHQ-9: Patient Health Questionnaire 9-item.

Table 4. Mean assessment scores across timepoints and linear mixed effects models.

aMeasureBaseline, mean (SD) 5 weeks, mean (SD) 8 weeksa, mean
(SD)

Parameter estimate(SE)t value (df)

P value

Hedges g

PHQ-9b

13.90 (4.25)

12.95 (5.86)

10.95 (5.77)−1.36 (0.58)−2.33(19.63).03

0.53

GAD-7c12.10 (4.24)

10.33 (5.42)

8.53 (5.18)−1.74 (0.65)−2.67(23.09).01

0.69

PROMIS Depression

29.05 (6.96)

24.89 (7.64)

21.21 (8.18)−3.84 (1.03)−3.72(20.32)

<.001

0.94

PROMIS Anxi- 25.25 (6.47)
ety

23.11 (5.92)

21.32 (10.32)−2.01 (0.83)−2.40(20.88).03

0.41

N=19 for week 8. Parameter estimates for main effects (time) correspond to changes across each time point (baseline, midpoint, and end of program).

b

PHQ-9: Patient Health Questionnaire 9-item.

c

GAD-7: Generalized Anxiety Disorder 7-item.

Discussionof treatment outcomes, such as cognition in older adults withdepressive symptoms.

Principal Findings

The qualitative findings provide an important context to help
elucidate specific aspects of the feasibility and acceptability ofthe program. As expected, the practices were found to be themost helpful component, which is consistent with previous
findings regarding guided self-management programs in older
adults [38]. The therapist was a critical aspect of the program,particularly for participants who desired human contact, support,
and encouragement or struggled with the practices or the app
itself. The support model mostly used pull support where users
reach out for help but also incorporated some push-based support(eg, feedback on practice reflections or weekly messages).
According to the Efficiency Model of Support, the limitationsof pull support include underutilization because of concerns
about burdening the supporter [39]. Our findings corroborate
this with participants describing uncertainty about how often
to communicate with the therapist and warrant further
consideration for program refinement.

Our findings suggest the feasibility and acceptability of an
8-week mobile app–based intervention for depressive andanxiety symptoms in a sample of middle-aged and older adults
approximately 30 years older than samples in most mobileintervention research. The low dropout rate (10%) and robust
engagement with the app mirrors findings with the Meru Health
Program in younger adults [19,20] and with an average dropout
rate of 17% for internet or mobile interventions [37]. Similarly,
the high satisfaction with the app, good ratings of usability, andoverall perception that the program was helpful by mostparticipants underscores the acceptability of this intervention
in older age groups.
Older adults with depression may benefit from mobileinterventions, such as the Meru Health Program, based on
significant reductions in depression and anxiety outcomes. Thisand the finding that 45% (9/20) of individuals achieved aclinically significant reduction in symptoms suggest that the
program may be beneficial to some middle-aged and older users.
The uncontrolled effect sizes were slightly larger than, but
comparable with, those found in a meta-analysis of mobileinterventions for depression [13]. Larger controlled studies areneeded to replicate our findings and to explore the moderators
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Group support was a unique, yet underutilized, program
component. Being part of a group facing similar struggles was
helpful to some in promoting understanding and a sense of not
being alone. Unfortunately, the limited response options forcommunicating with other members undermined the use of andinterest in this feature. Thus, this component should be
JMIR Form Res 2021 | vol. 5 | iss. 6 | e25808 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
considered for modification to increase its utilization. Suchfuture enhancements will need to balance the risks of providing
open-ended interactions, including potential breaches in
confidentiality.
Another potential refinement to the program that might
especially benefit older adults is an enhanced program
orientation, including an in-person or preview of the app, a
handout, instructional videos, or a combination of these. Suchan orientation may encourage greater exploration of the app, as
older users tend to be hesitant about making errors or pressing
incorrect buttons, resulting in less app exploration comparedwith younger users. Other suggestions for program
improvements included greater control over video-delivered
information (eg, ability to rewind videos to review content).
Program compatibility with tablets could help older users
experience dexterity and vision challenges. Including older users
in usability testing and research on digital health interventions
would ensure that these interventions are optimized for all users.
Furthermore, ensuring universal design is an important step
before dissemination in health care settings, such as primarycare that serves patients across the life span.

Limitations
Conclusions from our findings must consider the limitations of
the study. Foremost among these was the lack of a control
condition, leaving open the question of whether the intervention
was responsible for symptom improvement. Second, most of

Gould et al
our sample had at least 16 years of education, which may limit
the generalizability of the findings to those with less education.
Third, our sample for the pilot study was quite small, with 90%(18/20) of participants completing the intervention. Although
the sample included 40% (8/20) non-White individuals, becauseof the small overall sample size, further research is needed toevaluate the efficacy of this intervention in larger and even moreracially and ethnically diverse samples. Fourth, a standardized
mobile app rating scale or evidence-based usability measure
was not used to assess the acceptability or usability of the Meru
Health app. Fifth, mobile device proficiency for the sample wassubstantially higher than that evidenced in the initial study of
the MDPQ [21]; thus, the findings may not generalize toindividuals less proficient with mobile devices.

Conclusions
In summary, this study found evidence of feasibility andacceptability of a commercially available app, the Meru Health
Program, in a small sample of middle-aged and older users.
Furthermore, the findings show that some participants
experienced reduced psychiatric symptoms. Although larger,
controlled trials are needed to demonstrate efficacy, our findingsalign with research with younger adults and suggest that digital
health interventions may benefit and should be offered to adults
of all ages. If future research validates these preliminary
findings, mobile mental health interventions could be a valuable
tool in addressing unmet treatment needs and reducing theburden of depression among older adults.

Acknowledgments
Meru Health, Inc, provided funding for this project, but CEG retains full control over the data, analyses, and interpretation of thefindings. CEG is supported by a Career Development Award (IK2 RX001478) from the Department of Veterans Affairs
Rehabilitation Research and Development Service. The Stanford REDCap platform was developed and operated by the Stanford
Medicine Research IT team. The REDCap platform services at Stanford are subsidized by (1) Stanford School of Medicine
Research Office and (2) the National Center for Research Resources and the National Center for Advancing Translational Sciences,
National Institutes of Health, through grant UL1 TR001085. The views expressed in this paper are those of the authors and notnecessarily those of the Department of Veterans Affairs or the Federal Government.

Conflicts of Interest
CEG, CC, FM, and EK have no conflicts of interest to report. VFH is employed as the Head of Research at Meru Health, Inc,
receives a salary from the company, and owns the options of the company. KR serves as the Chief Executive Officer of Meru
Health, Inc and owns a large share of the company’s stocks.

Multimedia Appendix 1
Meru Health Program study user experience and interview.
[DOC File , 56 KB-Multimedia Appendix 1]

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Abbreviations
AUDIT-C: Alcohol Use Disorders Identification Test
CBT: cognitive behavioral therapy
GAD-7: Generalized Anxiety Disorder 7-item
MDD: major depressive disorder
MDPQ: Mobile Device Proficiency Questionnaire
MINI: Mini International Neuropsychiatric Interview
PHQ-9: Patient Health Questionnaire 9-item
PROMIS: Patient-Reported Outcomes Measurement Information System

Edited by G Eysenbach; submitted 17.11.20; peer-reviewed by E Parrish, F Abujarad; comments to author 04.01.21; revised version
received 02.02.21; accepted 31.05.21; published 29.06.21
Please cite as:
Gould CE, Carlson C, Ma F, Forman-Hoffman V, Ranta K, Kuhn E
Effects of Mobile App–Based Intervention for Depression in Middle-Aged and Older Adults: Mixed Methods Feasibility StudyJMIR Form Res 2021;5(6):e25808
URL: https://formative.jmir.org/2021/6/e25808
doi: 10.2196/25808
PMID: 34185000

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JMIR FORMATIVE RESEARCH

Gould et al©Christine E Gould, Chalise Carlson, Flora Ma, Valerie Forman-Hoffman, Kristian Ranta, Eric Kuhn. Originally published in
JMIR Formative Research (https://formative.jmir.org), 29.06.2021. 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 Formative Research, is properly
cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as thiscopyright and license information must be included.

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