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THIS PAPER IS IN THE FOLLOWING E-COLLECTION/THEME ISSUE:
Formative Evaluation of Digital Health Interventions (1401) mHealth for
Wellness, Behavior Change and Prevention (2216) Anxiety and Stress Disorders
(792) Users' and Patients' Needs for Mental Health Services (287)
Published on 15.4.2022 in Vol 6 , No 4 (2022) :April
Preprints (earlier versions) of this paper are available at
https://preprints.jmir.org/preprint/36794, first published January 26, 2022.
FEASIBILITY, ACCEPTABILITY, AND PRELIMINARY OUTCOMES OF A COGNITIVE BEHAVIORAL
THERAPY–BASED MOBILE MENTAL WELL-BEING PROGRAM (NOOM MOOD): SINGLE-ARM
PROSPECTIVE COHORT STUDY
FEASIBILITY, ACCEPTABILITY, AND PRELIMINARY OUTCOMES OF A COGNITIVE BEHAVIORAL
THERAPY–BASED MOBILE MENTAL WELL-BEING PROGRAM (NOOM MOOD): SINGLE-ARM
PROSPECTIVE COHORT STUDY
Authors of this article:
Meaghan McCallum 1 ; Annabell Suh Ho 1 ; Ellen Siobhan Mitchell 1 ;
Christine N May 1 ; Heather Behr 1, 2 ; Lorie Ritschel 3, 4 ; Kirk Mochrie
3 ; Andreas Michaelides 1
Article Authors Cited by Tweetations (8) Metrics
* Abstract
* Introduction
* Methods
* Results
* Discussion
* References
* Abbreviations
* Copyright
ORIGINAL PAPER
* Meaghan McCallum1, PhD ;
* Annabell Suh Ho1, PhD ;
* Ellen Siobhan Mitchell1, PhD ;
* Christine N May1, PhD ;
* Heather Behr1,2, PhD ;
* Lorie Ritschel3,4, PhD ;
* Kirk Mochrie3, PhD ;
* Andreas Michaelides1, PhD
1Academic Research, Noom Inc, New York, NY, United States
2Department of Integrative Health, Saybrook University, Pasadena, CA, United
States
3Triangle Area Psychology Clinic, Durham, NC, United States
4School of Medicine, University of North Carolina, Chapel Hill, NC, United
States
CORRESPONDING AUTHOR:
Ellen Siobhan Mitchell, PhD
Academic Research
Noom Inc
229 W 28th St, Fl 9
New York, NY, 10001
United States
Phone: 1 631 938 1248
Email: siobhan@noom.com
ABSTRACT
Background: The prevalence of anxiety, depression, and general distress has
risen in recent years. Mobile mental health programs have been found to provide
support to nonclinical populations and may overcome some of the barriers
associated with traditional in-person treatment; however, researchers have
voiced concerns that many publicly available mobile mental health programs lack
evidence-based theoretical foundations, peer-reviewed research, and sufficient
engagement from the public.
Objective: This study aimed to evaluate the feasibility, acceptability, and
preliminary outcomes of Noom Mood, a commercial mobile cognitive behavioral
therapy– and mindfulness-based program.
Methods: In this single-arm prospective cohort study, individuals who joined
Noom Mood between August and October 2021 completed surveys at baseline and
4-week follow-up. Per-protocol analyses included those who completed both
surveys (n=113), and intention-to-treat analyses included all participants
(N=185).
Results: A majority of the sample reported that the program is easy to use, they
felt confident recommending the program to a friend, and they perceived the
program to be effective at improving stress and anxiety. There were significant
improvements in anxiety symptoms, perceived stress, depressive feelings, emotion
regulation, and optimism in both the per-protocol and intention-to-treat
analyses (all P<.001). Participants reported benefiting most from learning
skills (eg, breathing and cognitive reframing techniques), interacting with the
program features, and gaining awareness of their emotions and thought patterns.
Participants also made a number of suggestions to improve product functionality
and usability.
Conclusions: Results suggest that Noom Mood is feasible and acceptable to
participants, with promising preliminary outcomes. Future studies should build
on these results to evaluate the effects of Noom Mood using more rigorous
designs.
JMIR Form Res 2022;6(4):e36794
doi:10.2196/36794
KEYWORDS
mHealth (622); mobile mental health (4); mental health (311); stress
(57); anxiety (139)
INTRODUCTION
The World Health Organization stresses the importance of mental health, which
they broadly define as a state of “well-being in which an individual realizes
his or her own abilities and can cope with the normal stresses of life” [Mental
health: Strengthening our response. World Health Organization. 2018 Mar 30.
URL:
https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response
[accessed 2021-12-01] 1]. Many individuals are affected by difficulties with
mental health [Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters
EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in
the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005
Jun;62(6):593-602. [CrossRef] [Medline]2]; for example, anxiety disorders are
highly prevalent worldwide and are estimated to affect 18% of individuals in the
United States alone [Remes O, Brayne C, van der Linde R, Lafortune L. A
systematic review of reviews on the prevalence of anxiety disorders in adult
populations. Brain Behav 2016 Jul;6(7):e00497 [FREE Full text] [CrossRef]
[Medline]3,Demyttenaere K, Bruffaerts R, Posada-Villa J, Gasquet I, Kovess V,
Lepine JP, WHO World Mental Health Survey Consortium. Prevalence, severity, and
unmet need for treatment of mental disorders in the World Health Organization
World Mental Health Surveys. JAMA 2004 Jun 02;291(21):2581-2590. [CrossRef]
[Medline]4]. Lifetime prevalence for depression is approximately 17% [Kessler
RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence
and age-of-onset distributions of DSM-IV disorders in the National Comorbidity
Survey Replication. Arch Gen Psychiatry 2005 Jun;62(6):593-602. [CrossRef]
[Medline]2]. Furthermore, it is increasingly recognized that the general
population can benefit from mental health support, regardless of whether
clinical thresholds for mental illness are met [Bakker D, Kazantzis N, Rickwood
D, Rickard N. Mental health smartphone apps: Review and evidence-based
recommendations for future developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE
Full text] [CrossRef] [Medline]5,Arango C, Díaz-Caneja CM, McGorry PD, Rapoport
J, Sommer IE, Vorstman JA, et al. Preventive strategies for mental health.
Lancet Psychiatry 2018 Jul;5(7):591-604. [CrossRef] [Medline]6]: as many as 57%
to 84% of US adults have reported subclinical but substantial amounts of stress
or worry in recent years [Canady VA. APA stress report amid COVID-19 points to
parental challenges. Mental Health Weekly. 2020 May 29. URL:
https://onlinelibrary.wiley.com/doi/10.1002/mhw.32385 [accessed 2022-04-09]
7,Witters D, Harter J. Worry and stress fuel record drop in US life
satisfaction. Gallup. 2020 May 08. URL:
https://news.gallup.com/poll/310250/worry-stress-fuel-record-drop-life-satisfaction.aspx
[accessed 2021-12-01] 8]. Estimates suggest that anxiety, depression, and stress
are associated with greater risk of mortality and hundreds of billions of
dollars in economic burden per year [Walker ER, McGee RE, Druss BG. Mortality in
mental disorders and global disease burden implications: A systematic review and
meta-analysis. JAMA Psychiatry 2015 Apr;72(4):334-341 [FREE Full text]
[CrossRef] [Medline]9,Whiteford A, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ,
Erskine HE, et al. Global burden of disease attributable to mental and substance
use disorders: Findings from the Global Burden of Disease Study 2010. Lancet
2013 Nov 09;382(9904):1575-1586. [CrossRef] [Medline]10].
Although a number of empirically supported treatments for mental health
difficulties are available, myriad barriers exist that make it difficult for
many people to access traditional in-person support, including cost, long
waiting times to see providers, and limited provider availability, especially
for individuals living in remote areas [Andrade LH, Alonso J, Mneimneh Z, Wells
JE, Al-Hamzawi A, Borges G, et al. Barriers to mental health treatment: Results
from the WHO World Mental Health surveys. Psychol Med 2014 Apr;44(6):1303-1317
[FREE Full text] [CrossRef] [Medline]11-Comer JS, Barlow DH. The occasional case
against broad dissemination and implementation: Retaining a role for specialty
care in the delivery of psychological treatments. Am Psychol 2014 Jan;69(1):1-18
[FREE Full text] [CrossRef] [Medline]15]. The COVID-19 pandemic has also
increased barriers to accessing in-person support, potentially increasing
willingness to seek digital support [Taylor CB, Fitzsimmons-Craft EE, Graham AK.
Digital technology can revolutionize mental health services delivery: The
COVID-19 crisis as a catalyst for change. Int J Eat Disord 2020
Jul;53(7):1155-1157 [FREE Full text] [CrossRef] [Medline]16,Atherly A, Van Den
Broek-Altenburg E, Hart V, Gleason K, Carney J. Consumer reported care deferrals
due to the COVID-19 pandemic, and the role and potential of telemedicine:
Cross-sectional analysis. JMIR Public Health Surveill 2020 Sep 14;6(3):e21607
[FREE Full text] [CrossRef] [Medline]17]. In addition, many individuals avoid
seeking treatment due to stigma or to mistrust of the mental health system more
generally [Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G,
et al. Barriers to mental health treatment: Results from the WHO World Mental
Health surveys. Psychol Med 2014 Apr;44(6):1303-1317 [FREE Full text] [CrossRef]
[Medline]11,Leong FTL, Kalibatseva Z. Cross-cultural barriers to mental health
services in the United States. Cerebrum 2011 Mar;2011:5 [FREE Full text]
[Medline]13].
In recent years, there has been a proliferation of interest in and development
of mobile mental health programs. Use of these programs has tripled in recent
years [Huckvale K, Nicholas J, Torous J, Larsen ME. Smartphone apps for the
treatment of mental health conditions: Status and considerations. Curr Opin
Psychol 2020 Dec;36:65-70 [FREE Full text] [CrossRef] [Medline]18], and multiple
reviews suggest that mobile mental health apps have the capacity to improve
mental health and emotion regulation in the general population [Eisenstadt M,
Liverpool S, Infanti E, Ciuvat RM, Carlsson C. Mobile apps that promote emotion
regulation, positive mental health, and well-being in the general population:
Systematic review and meta-analysis. JMIR Ment Health 2021 Nov 08;8(11):e31170
[FREE Full text] [CrossRef] [Medline]19,Hwang WJ, Ha JS, Kim MJ. Research trends
on mobile mental health application for general population: A scoping review.
Int J Environ Res Public Health 2021 Mar 02;18(5):2459 [FREE Full text]
[CrossRef] [Medline]20]. Mobile mental health has the potential to address many
of the aforementioned barriers to treatment [Hilty DM, Chan S, Hwang T, Wong A,
Bauer AM. Advances in mobile mental health: Opportunities and implications for
the spectrum of e-mental health services. Mhealth 2017 Aug;3:34 [FREE Full text]
[CrossRef] [Medline]21,Price M, Yuen EK, Goetter EM, Herbert JD, Forman EM,
Acierno R, et al. mHealth: A mechanism to deliver more accessible, more
effective mental health care. Clin Psychol Psychother 2014 Aug;21(5):427-436
[FREE Full text] [CrossRef] [Medline]22]; perhaps most importantly, mobile
mental health allows for support or psychoeducation that is not restricted by
time, location, or provider availability. In addition, digital (ie, via
smartphone) delivery increases accessibility and autonomy in allowing for
largely self-directed care [Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental
health smartphone apps: Review and evidence-based recommendations for future
developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE Full text] [CrossRef]
[Medline]5,Rathbone AL, Prescott J. The use of mobile apps and SMS messaging as
physical and mental health interventions: Systematic review. J Med Internet Res
2017 Aug 24;19(8):e295 [FREE Full text] [CrossRef] [Medline]23]. Such programs
facilitate self-monitoring of mood or activity, a well-known strategy to change
undesired behaviors [Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health
smartphone apps: Review and evidence-based recommendations for future
developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE Full text] [CrossRef]
[Medline]5]. Lastly, mobile platforms allow for objective measurement of
behavioral indicators, such as the number of articles read, and, therefore,
allow individuals to track which strategies are most effective in helping them
achieve behavioral change.
Despite this proliferation of readily accessible mobile mental health programs,
researchers have raised several concerns that merit attention and that can be
viewed through the lens of implementation science (see Proctor et al [Proctor E,
Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for
implementation research: Conceptual distinctions, measurement challenges, and
research agenda. Adm Policy Ment Health 2011 Mar;38(2):65-76 [FREE Full text]
[CrossRef] [Medline]24] for an in-depth discussion of implementation science
variables as they apply to outcome studies). First, many mobile health (mHealth)
programs available to the public are not based on evidence-based theoretical
frameworks [Marshall JM, Dunstan DA, Bartik W. Apps with maps-Anxiety and
depression mobile apps with evidence-based frameworks: Systematic search of
major app stores. JMIR Ment Health 2020 Jun 24;7(6):e16525 [FREE Full text]
[CrossRef] [Medline]25]. Moreover, users are self-selected, meaning that the
problems they are experiencing may or may not map onto the content including the
mobile app (ie, problems with appropriateness) [Marshall JM, Dunstan DA, Bartik
W. Apps with maps-Anxiety and depression mobile apps with evidence-based
frameworks: Systematic search of major app stores. JMIR Ment Health 2020 Jun
24;7(6):e16525 [FREE Full text] [CrossRef] [Medline]25]. Second, whether
evidence based or not, many programs are used briefly and then discarded (ie,
problems with adoption) or do not reach a broad enough segment of the population
to be useful (ie, problems with penetration) [Huckvale K, Nicholas J, Torous J,
Larsen ME. Smartphone apps for the treatment of mental health conditions: Status
and considerations. Curr Opin Psychol 2020 Dec;36:65-70 [FREE Full text]
[CrossRef] [Medline]18,Wasil AR, Weisz JR, DeRubeis RJ. Three questions to
consider before developing a mental health app. World Psychiatry 2020
Jun;19(2):252-253 [FREE Full text] [CrossRef] [Medline]26]. Research has found
that thousands of programs have been released on app stores that retain a very
limited number of active users over time; for example, studies have shown that
97% of users do not use these mental health apps at day 15 [Wasil AR, Weisz JR,
DeRubeis RJ. Three questions to consider before developing a mental health app.
World Psychiatry 2020 Jun;19(2):252-253 [FREE Full text] [CrossRef]
[Medline]26,Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with
mental health apps: Systematic search and panel-based usage analysis. J Med
Internet Res 2019 Sep 25;21(9):e14567 [FREE Full text] [CrossRef] [Medline]27].
This represents an obvious challenge for mental health programs, as intervention
engagement has been associated with better outcomes in a multitude of studies
[Bakker D, Rickard N. Engagement in mobile phone app for self-monitoring of
emotional wellbeing predicts changes in mental health: MoodPrism. J Affect
Disord 2018 Dec;227:432-442. [CrossRef] [Medline]28-Bakker D, Rickard N.
Engagement with a cognitive behavioural therapy mobile phone app predicts
changes in mental health and wellbeing: MoodMission. Aust Psychol 2020 Nov
12;54(4):245-260. [CrossRef]30]. Lastly, few of these mobile mental health
programs include a research component to evaluate feasibility, acceptability, or
outcomes of any sort; of programs based on theoretical frameworks, only
approximately 6.2% have associated peer-reviewed research [Marshall JM, Dunstan
DA, Bartik W. Apps with maps-Anxiety and depression mobile apps with
evidence-based frameworks: Systematic search of major app stores. JMIR Ment
Health 2020 Jun 24;7(6):e16525 [FREE Full text] [CrossRef] [Medline]25,Marshall
JM, Dunstan DA, Bartik W. Clinical or gimmickal: The use and effectiveness of
mobile mental health apps for treating anxiety and depression. Aust N Z J
Psychiatry 2020 Jan;54(1):20-28. [CrossRef] [Medline]31,Lee RA, Jung ME.
Evaluation of an mHealth app (DeStressify) on university students' mental
health: Pilot trial. JMIR Ment Health 2018 Jan 23;5(1):e2 [FREE Full text]
[CrossRef] [Medline]32].
As such, this study was designed to address these gaps in the literature by
examining the feasibility, acceptability, and preliminary outcomes of Noom Mood,
a widely available commercial mHealth program that incorporates evidence-based
recommendations for mobile mental health programs [Bakker D, Kazantzis N,
Rickwood D, Rickard N. Mental health smartphone apps: Review and evidence-based
recommendations for future developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE
Full text] [CrossRef] [Medline]5]. In particular, this study aims to contribute
to the substantial gap in the evidence base, identified by implementation
science researchers and review papers on mobile mental health, in data from
commercial programs [Marshall JM, Dunstan DA, Bartik W. Apps with maps-Anxiety
and depression mobile apps with evidence-based frameworks: Systematic search of
major app stores. JMIR Ment Health 2020 Jun 24;7(6):e16525 [FREE Full text]
[CrossRef] [Medline]25,Marshall JM, Dunstan DA, Bartik W. Clinical or gimmickal:
The use and effectiveness of mobile mental health apps for treating anxiety and
depression. Aust N Z J Psychiatry 2020 Jan;54(1):20-28. [CrossRef]
[Medline]31,Lecomte T, Potvin S, Corbière M, Guay S, Samson C, Cloutier B, et
al. Mobile apps for mental health issues: Meta-review of meta-analyses. JMIR
Mhealth Uhealth 2020 May 29;8(5):e17458 [FREE Full text] [CrossRef]
[Medline]33,Lau N, O'Daffer A, Colt S, Yi-Frazier JP, Palermo TM, McCauley E, et
al. Android and iPhone mobile apps for psychosocial wellness and stress
management: Systematic search in app stores and literature review. JMIR Mhealth
Uhealth 2020 May 22;8(5):e17798 [FREE Full text] [CrossRef] [Medline]34].
Another contribution of this study stems from Noom Mood’s inclusion of personal
coaching for guidance and implementation of cognitive behavioral therapy (CBT)
techniques, but not clinical therapy. Few studies have examined widely available
mental health programs guided by personal coaching; many existing studies
examine mental health programs that are entirely self-guided (ie, without
individualized coaching support), are designed to provide clinical therapy or
serve as an adjunct to therapy, or provide personalized coaching in other
contexts (eg, employer-provided coaching or for specific conditions) [Huberty J,
Green J, Glissmann C, Larkey L, Puzia M, Lee C. Efficacy of the mindfulness
meditation mobile app "Calm" to reduce stress among college students: Randomized
controlled trial. JMIR Mhealth Uhealth 2019 Jun 25;7(6):e14273 [FREE Full text]
[CrossRef] [Medline]35-Solness CL, Kroska EB, Holdefer PJ, O'Hara MW. Treating
postpartum depression in rural veterans using internet delivered CBT: Program
evaluation of MomMoodBooster. J Behav Med 2021 Aug;44(4):454-466 [FREE Full
text] [CrossRef] [Medline]38].
Noom Mood is a structured, skills-based approach to stress and anxiety
management. Noom Mood uses strategies from empirically supported treatments that
have been shown to improve mental health outcomes, such as anxiety, depression,
and stress (eg, CBT, dialectical behavior therapy [DBT], acceptance and
commitment therapy [ACT], and mindfulness-based stress reduction [MBSR])
[Hofmann SG, Asnaani A, Vonk IJJ, Sawyer AT, Fang A. The efficacy of cognitive
behavioral therapy: A review of meta-analyses. Cognit Ther Res 2012 Oct
1;36(5):427-440 [FREE Full text] [CrossRef] [Medline]39-Valentine SE, Bankoff
SM, Poulin RM, Reidler EB, Pantalone DW. The use of dialectical behavior therapy
skills training as stand-alone treatment: A systematic review of the treatment
outcome literature. J Clin Psychol 2015 Jan;71(1):1-20. [CrossRef] [Medline]43].
Importantly, preliminary evidence has shown that CBT and MBSR can be deployed on
a mobile platform and that these programs are associated with improvements in
mental well-being in nonclinical and clinical populations [Rathbone AL, Prescott
J. The use of mobile apps and SMS messaging as physical and mental health
interventions: Systematic review. J Med Internet Res 2017 Aug 24;19(8):e295
[FREE Full text] [CrossRef] [Medline]23,Yang E, Schamber E, Meyer RML, Gold JI.
Happier healers: Randomized controlled trial of mobile mindfulness for stress
management. J Altern Complement Med 2018 May;24(5):505-513. [CrossRef]
[Medline]44]; however, as described previously, more empirical evaluation is
needed of evidence-based, commercial programs. Program components include the
following: (1) a daily curriculum consisting of psychoeducational articles for
users to read, (2) individualized coaching offered through in-app messaging, (3)
weekly skills-based activities, and (4) a mood-logging feature. All four
components are expected to improve mental well-being (eg, reduce perceived
anxiety and depressive symptoms and perceived stress). The curriculum,
activities, and coaching were derived from evidence-based frameworks (ie, CBT,
DBT, ACT, and MBSR) that have been shown to be effective in improving these
outcomes, so these three components would be expected to be most directly
related to outcomes. The fourth component of mood logging is based on behavior
change techniques of self-monitoring, helping users to build self-awareness of
their mood and associated behaviors [Caldeira C, Chen Y, Chan L, Pham V, Chen Y,
Zheng K. Mobile apps for mood tracking: An analysis of features and user
reviews. AMIA Annu Symp Proc 2017;2017:495-504 [FREE Full text] [Medline]45].
More specifically, the daily curriculum was developed in collaboration with
clinical psychologists and was designed to translate evidence-based treatments
and psychoeducation into a format that is useful for individuals within a
self-help framework. For example, each day, participants are presented with a
short article that explains conceptual terms and principles (eg, cognitive
defusion from ACT), provides practical tips and quizzes to build knowledge, and
guides users through a relevant practical activity (eg, how to practice
cognitive defusion over the next week; Figure 1). Because of the utility of
skills-training activities that help to apply evidence-based principles into
daily life [Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health
smartphone apps: Review and evidence-based recommendations for future
developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE Full text] [CrossRef]
[Medline]5,Ahtinen A, Mattila E, Välkkynen P, Kaipainen K, Vanhala T, Ermes M,
et al. Mobile mental wellness training for stress management: Feasibility and
design implications based on a one-month field study. JMIR Mhealth Uhealth 2013
Jul;1(2):e11 [FREE Full text] [CrossRef] [Medline]46,Levin ME, Haeger J, Pierce
B, Cruz RA. Evaluating an adjunctive mobile app to enhance psychological
flexibility in acceptance and commitment therapy. Behav Modif 2017
Nov;41(6):846-867. [CrossRef] [Medline]47], Noom Mood introduces individuals to
a short 10- to 15-minute practical activity based on evidence-based frameworks,
such as breathing techniques and cognitive reframing at the beginning of each
week. The activity is implemented for 1 week, with a practice on day 7 in which
individuals reflect on the skill learned and how well it worked for them (Figure
1). Lastly, Noom Mood includes a messaging feature that allows participants to
communicate directly with health coaches (Figure 1). Coaches help users to
understand and engage in activities, encourage reflection and awareness of
patterns, and provide validation for emotional experiences based on CBT
techniques. Coaching protocols were adapted to this mental well-being context
from the Noom weight management program, for which coaching has been refined and
tested and shown to provide guidance on activities, emotional self-awareness,
and emotional validation [Kim H, Tietsort C, Posteher K, Michaelides A,
Toro-Ramos T. Enabling self-management of a chronic condition through
patient-centered coaching: A case of an mHealth diabetes prevention program for
older adults. Health Commun 2020 Dec;35(14):1791-1799. [CrossRef] [Medline]48].
Noom Mood coaches are trained in CBT techniques but are not licensed clinicians,
as Noom Mood does not provide clinical assessment, diagnoses, or treatment and
is not a replacement for therapy. The coaching feature was included to address
concerns that have been cited in previous studies of evidence-based programs
[Kim H, Tietsort C, Posteher K, Michaelides A, Toro-Ramos T. Enabling
self-management of a chronic condition through patient-centered coaching: A case
of an mHealth diabetes prevention program for older adults. Health Commun 2020
Dec;35(14):1791-1799. [CrossRef] [Medline]48-Mohr DC, Weingardt KR, Reddy M,
Schueller SM. Three problems with current digital mental health research...and
three things we can do about them. Psychiatr Serv 2017 May 01;68(5):427-429
[FREE Full text] [CrossRef] [Medline]50]. Specifically, human contact from
remote coaches within otherwise self-guided digital programs may encourage
engagement and improve outcomes [Kim H, Tietsort C, Posteher K, Michaelides A,
Toro-Ramos T. Enabling self-management of a chronic condition through
patient-centered coaching: A case of an mHealth diabetes prevention program for
older adults. Health Commun 2020 Dec;35(14):1791-1799. [CrossRef]
[Medline]48,Andersson G, Carlbring P, Berger T, Almlöv J, Cuijpers P. What makes
internet therapy work? Cogn Behav Ther 2009 Jan;38 Suppl 1:55-60. [CrossRef]
[Medline]51,Andersson G, Cuijpers P. Internet-based and other computerized
psychological treatments for adult depression: A meta-analysis. Cogn Behav Ther
2009 Dec;38(4):196-205. [CrossRef] [Medline]52]. One randomized controlled trial
(RCT) found that engagement check-ins from coaches improved engagement in a
web-based depression program [Mohr DC, Duffecy J, Ho J, Kwasny M, Cai X, Burns
MN, et al. A randomized controlled trial evaluating a manualized TeleCoaching
protocol for improving adherence to a web-based intervention for the treatment
of depression. PLoS One 2013 Aug;8(8):e70086 [FREE Full text] [CrossRef]
[Medline]53].
Figure 1. Screenshots of the Noom Mood program. View this figure
The first step in evaluating any new mHealth platform is to investigate
stakeholders’ views on the feasibility and acceptability of the proposed product
[Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al.
Outcomes for implementation research: Conceptual distinctions, measurement
challenges, and research agenda. Adm Policy Ment Health 2011 Mar;38(2):65-76
[FREE Full text] [CrossRef] [Medline]24,Mohr DC, Weingardt KR, Reddy M,
Schueller SM. Three problems with current digital mental health research...and
three things we can do about them. Psychiatr Serv 2017 May 01;68(5):427-429
[FREE Full text] [CrossRef] [Medline]50]. Feasibility is defined as the extent
to which end users feel that they could and would use the product in their lives
for the purposes for which it was designed [Weiner BJ, Lewis CC, Stanick C,
Powell BJ, Dorsey CN, Clary AS, et al. Psychometric assessment of three newly
developed implementation outcome measures. Implement Sci 2017 Dec 29;12(1):108
[FREE Full text] [CrossRef] [Medline]54]. Acceptability is defined as the extent
to which stakeholders find the product satisfactory with regard to its content
and perceived credibility [Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN,
Clary AS, et al. Psychometric assessment of three newly developed implementation
outcome measures. Implement Sci 2017 Dec 29;12(1):108 [FREE Full text]
[CrossRef] [Medline]54]. Results from feasibility and acceptability testing are
then used to refine and update the platform to align with stakeholders’
suggestions more closely.
The primary goal of this study was to evaluate the feasibility and acceptability
of Noom Mood, as well as to gather preliminary data on whether the program might
be associated with improved well-being. We hypothesized that users would find
the platform to be feasible and acceptable. Furthermore, we hypothesized that
participants who used the program would report some benefit in terms of improved
anxiety symptoms, stress, depressive feelings, emotion regulation, and optimism
by the end of the 4-week study.
METHODS
A single-arm prospective cohort design was used to test feasibility and
acceptability of Noom Mood, as well as initial symptom and well-being outcomes.
ETHICS APPROVAL
The study was approved by the Advarra Institutional Review Board (protocol No.
00055306).
PROCEDURE AND PARTICIPANTS
Participants were recruited from the pool of individuals who had voluntarily
signed up for the Noom Mood program. A randomly selected subset of adults who
voluntarily enrolled in the Noom Mood program between August and October 2021
were invited to participate. All participants provided informed consent prior to
participation. Inclusion criteria for participants were as follows: located
within the United States, English speaking, and aged 18 years or older.
Participants were invited to complete the baseline questionnaire within 1
business day of signing up for the Noom Mood program. Those who completed the
baseline questionnaire were invited to complete the follow-up survey 4 weeks
later. Study completers were compensated with a US $20 gift card for their
participation. Participants did not receive the program for free during or after
the study. The entire study occurred remotely, including online administration
of surveys via email.
NOOM MOOD PROGRAM
The Noom Mood program was deployed as described above. At the time of this
study, approximately 15 psychoeducational articles were presented to
participants each week. In addition to the curriculum, participants had access
to mood-logging features, and they were encouraged by coaches to engage in the
curriculum and to log their mood once per day.
MEASURES
FEASIBILITY
Feasibility was assessed at 4-week follow-up.
SYSTEM USABILITY SCALE
The System Usability Scale (SUS) [Brooke J. SUS: A 'quick and dirty' usability
scale. In: Thomas B, Weerdmeester BA, McClleland IL, Jordan PW, editors.
Usability Evaluation in Industry. London, UK: Taylor & Francis Ltd;
1996:189-194.55] is a 10-item scale assessing stakeholders’ views of ease of
use. Items were modified to substitute “program” for “system.” Participants were
asked to rate their agreement with each usability statement (eg, “I thought the
program was easy to use”) on a scale of 1 (“strongly disagree”) to 5 (“strongly
agree”). After reverse-scoring relevant items, sum scores were multiplied by 2.5
to create a final score ranging from 0 to 100. Research indicates that SUS
scores above 68 are considered above average and scores below 68 are below
average. Internal reliability for the SUS was excellent (α=.90).
PROGRAM ENGAGEMENT DATA
As in past work [Schlosser DA, Campellone TR, Truong B, Anguera JA, Vergani S,
Vinogradov S, et al. The feasibility, acceptability, and outcomes of PRIME-D: A
novel mobile intervention treatment for depression. Depress Anxiety 2017
Jun;34(6):546-554 [FREE Full text] [CrossRef] [Medline]56], feasibility was also
evaluated via the amount of time participants spent engaging with the program.
Engagement data consisted of usage and self-report data recorded by the program
for 4 weeks. Self-report and usage data were collected by the mobile program and
stored on a secured cloud server from Amazon Web Services [Torous J, Kiang MV,
Lorme J, Onnela J. New tools for new research in psychiatry: A scalable and
customizable platform to empower data driven smartphone research. JMIR Ment
Health 2016 May 05;3(2):e16 [FREE Full text] [CrossRef] [Medline]57]. Data were
deidentified prior to extraction from the database. Engagement measures included
the frequency with which participants completed mood logs, number of times the
app was opened, number of articles read, number of messages sent to the coach,
and number of activities completed. Data were also extracted to evaluate the
number of days the user was active, which was defined as the number of days with
at least one in-app action. In order to measure real-world engagement,
participants were not given specific minimum engagement requirements to remain
in the study.
ACCEPTABILITY
Acceptability was assessed at 4-week follow-up.
CREDIBILITY AND EXPECTANCY QUESTIONNAIRE
The Credibility and Expectancy Questionnaire (CEQ) [Devilly GJ, Borkovec TD.
Psychometric properties of the credibility/expectancy questionnaire. J Behav
Ther Exp Psychiatry 2000 Jun;31(2):73-86. [CrossRef]58] is a 6-item scale that
was originally designed to assess perceptions of treatment credibility and
expectancy for improvement in psychotherapy. To render the scale more
appropriate for use in this study, questionnaire items were modified slightly
(ie, “program” was substituted for “therapy” and “stress and anxiety” was
substituted for “symptoms”). Items in the CEQ range either from 1 to 9 or from 0
to 100, depending on the item. In line with the CEQ’s factor structure and
following previous work [Thompson-Hollands J, Bentley KH, Gallagher MW, Boswell
JF, Barlow DH. Credibility and outcome expectancy in the unified protocol:
Relationship to outcomes. J Exp Psychopathol 2014 Mar 30;5(1):72-82.
[CrossRef]59], we computed average credibility and expectancy scores reflected
by the first three and last three items of the scale, respectively. Internal
reliability was excellent (credibility subscale: α=.90; expectancy subscale:
α=.93).
PROGRAM SATISFACTION QUESTIONNAIRE
We asked the following open-ended questions: (1) What is the main benefit you
received from Noom’s stress and anxiety management program? (2) How can we
improve Noom’s stress and anxiety management program for you? (3) What was the
most helpful part of the program? and (4) What was the least helpful part of the
program? Because of the variety of answers possible, content analysis was used
to code each response into categories and calculate the percentage of responses
allocated to each category. The categories were created using latent Dirichlet
allocation (LDA), a machine learning approach for automatic clustering of text
data [Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res
2003;3:993-1022 [FREE Full text]60]. LDA is an unsupervised approach that
automatically identifies latent clusters of words (ie, categories) that cluster
within unclassified data. Each word cluster was assigned a label, or category
name, by a master coder with experience with the program. For each question,
each participant response was given a score (0 or 1) for each category since one
response could apply to multiple categories. Interrater reliability between the
master coder and another coder blind to the study’s hypotheses and design ranged
from 0.72 to 1.0 for all categories, suggesting good to excellent reliability
[Landis JR, Koch GG. The measurement of observer agreement for categorical data.
Biometrics 1977 Mar;33(1):159. [CrossRef]61].
SYMPTOM AND WELL-BEING OUTCOMES
Symptom and well-being outcomes were assessed at baseline and 4-week follow-up.
7-ITEM GENERALIZED ANXIETY DISORDER SCALE
The 7-item Generalized Anxiety Disorder scale (GAD-7) [Spitzer RL, Kroenke K,
Williams JBW, Löwe B. A brief measure for assessing generalized anxiety
disorder: The GAD-7. Arch Intern Med 2006 May 22;166(10):1092-1097. [CrossRef]
[Medline]62] is a 7-item scale that assesses the extent to which individuals
experience symptoms of anxiety (eg, “Feeling nervous, anxious, or on edge”) on a
scale of 0 (“not at all”) to 3 (“nearly every day”). Internal reliability for
the GAD-7 was good (α=.82 and α=.87 for baseline and follow-up, respectively).
4-ITEM PERCEIVED STRESS SCALE
The 4-item Perceived Stress Scale (PSS-4) [Cohen S, Kamarck T, Mermelstein R. A
global measure of perceived stress. J Health Soc Behav 1983:385-396. [CrossRef]
[Medline]63] is a 4-item scale assessing the frequency with which individuals
experience various symptoms of stress (eg, “How often have you felt that you
were unable to control the important things in your life?”) on a scale of 0
(“never”) to 4 (“very often”). Internal reliability for the PSS-4 was adequate
(α=.68 and α=.69 for baseline and follow-up, respectively).
8-ITEM PATIENT HEALTH QUESTIONNAIRE DEPRESSION SCALE
The 8-item Patient Health Questionnaire depression scale (PHQ-8) [Kroenke K,
Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure
of current depression in the general population. J Affect Disord 2009
Apr;114(1-3):163-173. [CrossRef] [Medline]64] is an 8-item scale that assesses
the extent to which participants experience feelings of depression (eg, “feeling
down, depressed, or hopeless” or “little interest or pleasure in doing things”)
on a scale of 0 (“not at all”) to 3 (“nearly every day”). Internal reliability
for the PHQ-8 was good (α=.84 and α=.85 for baseline and follow-up,
respectively).
DIFFICULTIES IN EMOTION REGULATION SCALE–SHORT FORM
The Difficulties in Emotion Regulation Scale–Short Form (DERS-SF) [Gratz K,
Roemer L. Multidimensional assessment of emotion regulation and dysregulation:
Development, factor structure, and initial validation of the Difficulties in
Emotion Regulation Scale. J Psychopathol Behav Assess 2004 Mar;26(1):41-54.
[CrossRef]65,Victor S, Klonsky E. Validation of a brief version of the
Difficulties in Emotion Regulation Scale (DERS-18) in five samples. J
Psychopathol Behav Assess 2016 May 13;38(4):582-589. [CrossRef]66] is an 18-item
scale assessing emotion dysregulation. It comprises six subscales: emotional
awareness, clarity about the nature of one’s emotions, acceptance of one’s
emotions, access to effective emotion regulation strategies, ability to engage
in goal-directed activities while experiencing negative emotions, and ability to
manage one’s impulses during negative emotions. These subscales (α=.74-.91 and
α=.76-.91) and the DERS-SF total score (α=.89 at both time points) demonstrated
good internal consistency at baseline and follow-up, respectively.
LIFE ORIENTATION TEST–REVISED
The Life Orientation Test–Revised (LOT-R) [Scheier MF, Carver CS, Bridges MW.
Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and
self-esteem): A reevaluation of the Life Orientation Test. J Pers Soc Psychol
1994 Dec;67(6):1063-1078. [CrossRef] [Medline]67] is a 10-item scale that
assesses trait optimism. Individuals are asked to rate their agreement with each
statement (eg, “In uncertain times, I usually expect the best.”) on a scale of 0
(“strongly disagree”) to 4 (“strongly agree”). Internal reliability for the
LOT-R was good (α=.86 and α=.85 at baseline and follow-up, respectively).
STATISTICAL ANALYSIS
Analyses were conducted in SPSS software (version 27; IBM Corp). For
acceptability and feasibility, survey responses were descriptively analyzed with
mean scores and percentages of participants that chose each response. For
open-ended acceptability responses, content-analyzed categories are presented
descriptively with the percentage of responses that fall into each category.
Descriptive statistics were also conducted for engagement measures to evaluate
feasibility. For preliminary outcomes, paired 2-tailed t tests were conducted to
evaluate changes on all quantitative variables from baseline to week 4. Both
per-protocol and intention-to-treat analyses were conducted. The per-protocol
sample consisted of participants who completed both assessments (n=113) and
included those who started the program but stopped using it. Intention-to-treat
analyses included data from all participants who began the study (N=185);
baseline scores were carried forward for participants who did not complete the
week-4 assessment. Effect sizes were calculated using Cohen d [Cohen J.
Statistical Power Analysis for the Behavioral Sciences. 2nd edition. New York,
NY: Routledge; 1988.68].
RESULTS
PARTICIPANT CHARACTERISTICS
Participants’ demographic characteristics are presented in Table 1. A total of
185 unique Noom Mood users enrolled in the study and completed the baseline
survey. Of these, 113 (62.1%) participants completed the follow-up survey.
Participants who completed both baseline and follow-up surveys did not differ
significantly from those who completed only the baseline survey in terms of any
demographic variables or baseline survey values.
Table 1. Participant characteristics.
DemographicsPer-protocol sample (n=113)Intention-to-treat sample (N=185)Age
(years), mean (SD)36.8 (9.8)37.3 (10.4)Gender, n (%)
Male15 (13.3)32 (17.3)
Female94 (83.2)141 (76.2)
Other2 (1.8)3 (1.6)
Prefer not to say or N/Aa2 (1.8)9 (4.9)Ethnicity, n (%)
Hispanic13 (11.5)20 (10.8)
Not Hispanic97 (85.8)153 (82.7)
Prefer not to say or N/A3 (2.7)12 (6.5)Race, n (%)
White99 (87.6)153 (82.7)
Black or African American5 (4.4)7 (3.8)
Asian or Pacific Islander3 (2.7)11 (5.9)
Other0 (0)1 (0.5)
Prefer not to say or N/A6 (5.3)13 (7.0)Employment status, n (%)
Employed 88 (77.8)144 (77.8)
Not employed12 (10.6)18 (9.7)
Retired1 (0.9)2 (1.1)
Disabled5 (4.4)6 (3.2)
Student5 (4.4)6 (3.2)
Prefer not to say or N/A2 (1.8)9 (4.9)Education, n (%)
High school, GEDb, or less education7 (6.2)10 (5.4)
Some college or associate degree24 (21.2)37 (20.0)
College graduate45 (39.8)67 (36.2)
Graduate degree 35 (31.0)62 (33.5)
Prefer not to say or N/A2 (1.8)9 (4.9)
aN/A: not applicable.
bGED: General Education Development.
FEASIBILITY
Responses to the SUS are presented in Table 2. As noted above, scores of 68 or
higher on the SUS indicate above-average ratings of system usability. A majority
(79/109, 72.5%) of participants had overall system usability scores of 68 or
higher (mean 77.40, SD 19.45), which is considered an indication of good
usability [Thompson-Hollands J, Bentley KH, Gallagher MW, Boswell JF, Barlow DH.
Credibility and outcome expectancy in the unified protocol: Relationship to
outcomes. J Exp Psychopathol 2014 Mar 30;5(1):72-82. [CrossRef]59]. Most
participants reported that the program was easy to use (85/110, 77.3%), and they
thought that other people would be able to learn to use the program very quickly
(93/109, 85.3%).
Program engagement data are presented in Table 3. Engagement data are presented
as weekly averages (ie, the number of times the participant engaged in the
behavior over the course of the study divided by the total number of weeks).
Participants engaged within the app several times per week on average. Over 4
weeks, the per-protocol sample averaged 14.1 (SD 9.02) app opens, with 2 mean
app opens per week. They had an average of 12.1 days with an in-app action,
amounting to 1.7 active days per week. The intention-to-treat sample opened the
app, on average, 13.7 (SD 8.6) times over 4 weeks, with an average of 1.96 app
opens per week. They completed at least one in-app action on an average of 11.2
(SD 8.7) days, which amounted to 1.6 active days per week.
Table 2. Participants reporting good feasibility and acceptability.
Survey measureaValueSystem Usability Scale item, n (%)
I would like to use this program frequently. (n=109)62 (56.9)
I found the program unnecessarily complex.b (n=110)81 (73.6)
I thought the program was easy to use. (n=110)85 (77.3)
I would need the support of a technical person to be able to use this program.b
(n=108)95 (88.0)
I found the various functions in this program were well integrated. (n=109)76
(69.7)
I thought there was too much inconsistency in the program.b (n=109)87 (79.8)
I would imagine that most people would learn to use this program very
quickly. (n=109)93 (85.3)
I found the program very cumbersome to use.b (n=109)77 (70.6)
I felt very confident using the program. (n=108)78 (72.2)
I needed to learn a lot of things before I could get going with the program.b
(n=107)89 (83.2)System Usability Scale score of 68 or higher (n=109), n (%)79
(72.5)System Usability Scale overall score, mean (SD)77.4 (19.4)Credibility and
Expectancy Questionnaire item, n (%)
At this point, how logical does the program seem to you? (n=110)101 (91.8)
How successful do you think this program was in reducing stress and
anxiety? (n=109)83 (76.1)
How confident would you be in recommending this program to a friend who
experiences stress and anxiety? (n=108)87 (80.6)
By the end of the program, how much improvement in stress and anxiety do you
think will occur?c (n=108)63 (58.3)
At this point, how much do you really feel that the program will help to reduce
stress and anxiety? (n=108)85 (78.7)
By the end of this program, how much improvement in stress and anxiety do you
feel will occur?c (n=107)62 (57.9)
aThe table includes participants who chose 4 or greater (out of 5) on the System
Usability Scale or 5 or greater (out of 9) on the Credibility and Expectancy
Questionnaire, except where indicated.
bThese participants chose 2 or less (out of 5) on the System Usability Scale.
cThese participants chose at least 50% out of 100%.
Table 3. Average total engagement over 4 weeks.
Type of engagementPer-protocol sample (n=110)a, mean (SD)Intention-to-treat
sample (n=181)a, mean (SD)App opens14.11 (9.02)13.72 (8.60)Articles read37.12
(28.87)34.39 (27.44)Mood logs10.59 (9.54)10.00 (10.79)Days with one in-app
action12.14 (9.03)11.24 (8.68)Messages sent to coach9.71 (10.33)8.24
(9.09)Activitiesb1.33 (2.68) 1.30 (2.54)
aSample sizes represent all participants for whom matching data from the
database could be identified.
bActivities were calculated over 3 weeks because one offline activity was not
tracked by the program.
ACCEPTABILITY
Responses to the CEQ are presented in Table 2. Of note, the table displays the
frequency and percentage of participants who chose at least a 5 (“somewhat”) out
of 9 (“very much”) on the CEQ. The vast majority of participants (101/110,
91.8%) rated the program as at least somewhat logical (mean 7.1, SD 1.9, range
1-9). Most (83/109, 76.1%) thought the program was at least somewhat successful
at reducing stress and anxiety (mean 5.6, SD 2.2, range 1-9). Many participants
(87/108, 80.6%) also felt at least somewhat confident in recommending the
program to a friend (mean 6.1, SD 2.3, range 1-9). Most participants (85/108,
78.7%) felt the program would help to reduce stress and anxiety at least
somewhat (mean 5.7, SD 2.3), with more than half (63/108, 58.3%) expecting it to
reduce their stress or anxiety by 50% or more (mean 4.9, SD 2.5, with 0
referring to 0% and 10 referring to 100%).
Responses to the Program Satisfaction Questionnaire are presented in Table 4.
Participants reported benefiting most from the skills and techniques they
learned or practiced (eg, breathing techniques and thought reframing; 38/106,
35.8%). Participants also reported benefiting from the program’s features or
capabilities (eg, mood tracking and articles; 31/106, 29.2%) and greater
awareness (eg, learning and reflection) encouraged by the program (30/106,
28.3%). Specifically, participants found the articles (18/106, 17.0%), coaching
(18/106, 17.0%), and qualities of the program (eg, manageable, convenient, and
“great” attitude; 16/106, 15.1%) to be the most helpful parts of Noom Mood.
For potential areas of improvement, most participants did not provide a response
or indicated that they had no suggested improvements (37/106, 34.9%). The next
most common response was “other” (21/106, 19.8%), or participants requested a
new feature or program idea (19/106, 17.9%). “Other” responses included
increasing the frequency of reminders, expanding areas of content (eg, support
for procrastination), and slowing the pace of tasks. Participants also preferred
a lower cost (16/106, 15.1%), with some mentioning the potential to be
reimbursed, as well as a more personalized experience (9/106, 8.5%) and greater
flexibility (9/106, 8.5%), such as the ability to progress while skipping
articles, accessing future articles, or repeating an activity for another week.
When asked to describe the least helpful parts of Noom Mood, most participants
did not provide a response (40/106, 37.7%). The next most common response was
“other” (21/106, 19.8%); responses noted that the program contained too much
repetition and that the pacing of the program needed improvement. Lastly, some
participants (17/106, 16.0%) described coaching as the least helpful aspect of
the program, noting that they would prefer to interact with a coach with
specialized expertise or to receive more personalized responses.
Table 4. Response frequencies in each category for the Program Satisfaction
Questionnaire.
CategoryParticipant responses (n=106), n (%)aMain benefit of Noom Mood
Skills and techniques38 (35.8)
Program features or capabilities31 (29.2)
Awareness (ie, learning and reflection)30 (28.3)
Emotional experience and management27 (25.5)
Other18 (17.0)
None or no response15 (14.2)Areas to improve
None or no response37 (34.9)
Other21 (19.8)
New feature or program idea19 (17.9)
Cost16 (15.1)
Coaching15 (14.2)
Personalization9 (8.5)
Flexibility9 (8.5)
Articles6 (5.7)
Activities3 (2.8)Most helpful part of Noom Mood
Coaching18 (17.0)
Articles18 (17.0)
None or no response17 (16.0)
Qualities of the program16 (15.1)
Skills and techniques15 (14.2)
Activities14 (13.2)
Awareness (ie, learning and reflection)9 (8.5)
Other9 (8.5)
Mood tracking5 (4.7)
Everything3 (2.8)Least helpful part of Noom Mood
None or no response40 (37.7)
Other21 (19.8)
Coaching17 (16.0)
Activities9 (8.5)
Mood tracking7 (6.6)
Articles6 (5.7)
Personalization and interactivity5 (4.7)
Cost4 (3.8)
Everything2 (1.9)
aEach response could be placed in more than one category. Categories were
derived from individuals’ open-ended responses.
SYMPTOM AND WELL-BEING OUTCOMES
From baseline to 4 weeks, there was a significant reduction in anxiety symptoms
for both per-protocol samples (Table 5; t112=10.92, P<.001, d=1.03) and
intention-to-treat samples (t184=9.48, P<.001, d=0.70) with large and medium
effect sizes, respectively. There was also a significant improvement in
perceived stress (per-protocol sample: t112=7.69, P<.001, d=0.72;
intention-to-treat sample: t184=7.09, P<.001, d=0.52) and depressive feelings
(per-protocol sample: t110=7.88, P<.001, d=0.75; intention-to-treat sample:
t181=7.40, P<.001, d=0.55) with medium effect sizes. Finally, there were
significant improvements in emotion regulation (per-protocol sample: t105=5.93,
P<.001, d=0.58; intention-to-treat sample: t178=5.79, P<.001, d=0.43) and
optimism (per-protocol sample: t104=–5.04, P<.001, d=–0.49; intention-to-treat
sample: t175=–5.15, P<.001, d=–0.39) with small to medium effect sizes.
Table 5. Symptom and well-being outcomes from baseline to 4 weeks.
OutcomePer-protocol sample (n=113)aIntention-to-treat sample (N=185)b
Baseline, mean (SD)4 weeks, mean (SD)ΔMean (% change)cP valueEffect
sizedBaseline, mean (SD)4 weeks, mean (SD)ΔMean (% change)cP valueEffect
sizedAnxiety symptoms (GAD-7e)13.30 (4.31)8.54 (4.61)–4.76
(–35.81)<.0011.0313.28 (4.39)10.18 (5.14)–3.10 (–23.32)<.0010.70Perceived stress
(PSS-4f)8.96 (2.39)7.08 (2.29)–1.88 (–21.03)<.0010.728.89 (2.41)7.73 (2.48)–1.16
(–13.07)<.0010.52Depressive feelings (PHQ-8g)11.67 (5.47)7.77 (4.98)–3.90
(–33.39)<.0010.7511.99 (5.55)9.40 (5.66)–2.59 (–21.61)<.0010.55Emotion
regulation (DERS-SFh)45.97 (11.86)39.39 (11.30)–6.57 (–14.30) <.0010.5847.01
(13.09)42.95 (13.49)–4.05 (–8.63) <.0010.43Optimism (LOT-Ri)7.05 (3.49)8.16
(3.15)1.11 (15.75)<.0010.497.33 (3.57)8.09 (3.39)0.75 (10.30) <.0010.39
aPer-protocol analyses only included participants who completed both survey
assessments.
bFor intention-to-treat analyses, baseline responses were carried forward for
nonresponders.
cNegative values indicate decreases compared to baseline.
dEffect sizes constitute Cohen d.
eGAD-7: 7-item Generalized Anxiety Disorder scale.
fPSS-4: 4-item Perceived Stress Scale.
gPHQ-8: 8-item Patient Health Questionnaire depression scale.
hDERS-SF: Difficulties in Emotion Regulation Scale–Short Form; negative values
on the DERS-SF indicate better emotional regulation (ie, fewer difficulties with
emotional regulation).
iLOT-R: Life Orientation Test–Revised; positive values on the LOT-R indicate
more optimism.
DISCUSSION
PRINCIPAL FINDINGS
In reviews of mental health programs, researchers have voiced concerns about
limited published research on commercial programs, and that programs either have
limited public engagement or are not based on evidence-based theory [Huckvale K,
Nicholas J, Torous J, Larsen ME. Smartphone apps for the treatment of mental
health conditions: Status and considerations. Curr Opin Psychol 2020
Dec;36:65-70 [FREE Full text] [CrossRef] [Medline]18,Marshall JM, Dunstan DA,
Bartik W. Apps with maps-Anxiety and depression mobile apps with evidence-based
frameworks: Systematic search of major app stores. JMIR Ment Health 2020 Jun
24;7(6):e16525 [FREE Full text] [CrossRef] [Medline]25-Baumel A, Muench F, Edan
S, Kane JM. Objective user engagement with mental health apps: Systematic search
and panel-based usage analysis. J Med Internet Res 2019 Sep 25;21(9):e14567
[FREE Full text] [CrossRef] [Medline]27,Marshall JM, Dunstan DA, Bartik W.
Clinical or gimmickal: The use and effectiveness of mobile mental health apps
for treating anxiety and depression. Aust N Z J Psychiatry 2020 Jan;54(1):20-28.
[CrossRef] [Medline]31,Lee RA, Jung ME. Evaluation of an mHealth app
(DeStressify) on university students' mental health: Pilot trial. JMIR Ment
Health 2018 Jan 23;5(1):e2 [FREE Full text] [CrossRef] [Medline]32]. Given the
identified need for evidence from this type of commercial program [Marshall JM,
Dunstan DA, Bartik W. Apps with maps-Anxiety and depression mobile apps with
evidence-based frameworks: Systematic search of major app stores. JMIR Ment
Health 2020 Jun 24;7(6):e16525 [FREE Full text] [CrossRef] [Medline]25,Marshall
JM, Dunstan DA, Bartik W. Clinical or gimmickal: The use and effectiveness of
mobile mental health apps for treating anxiety and depression. Aust N Z J
Psychiatry 2020 Jan;54(1):20-28. [CrossRef] [Medline]31], this pilot study
evaluated the feasibility, acceptability, and preliminary outcomes of Noom Mood,
which is widely publicly available, based on CBT and MBSR techniques, designed
to encourage engagement among the general public, and includes personal
coaching. Our results suggest that the program was usable, feasible, and
acceptable to participants. In addition, self-reported anxiety symptoms, stress,
depressive feelings, emotion regulation, and optimism improved from baseline to
4 weeks.
FEASIBILITY AND ACCEPTABILITY
FEASIBILITY
Overall, participants rated the program as feasible. The average system
usability score was 77.4, which surpasses the threshold for good usability
[Brooke J. SUS: A retrospective. J Usability Stud 2013 Feb;8(2):29-40 [FREE Full
text]69], and more than 75% of participants reported that the program was easy
to use. These scores are in line with feasibility and usability scores from
other mobile programs [Deady M, Johnston D, Milne D, Glozier N, Peters D, Calvo
R, et al. Preliminary effectiveness of a smartphone app to reduce depressive
symptoms in the workplace: Feasibility and acceptability study. JMIR Mhealth
Uhealth 2018 Dec 04;6(12):e11661 [FREE Full text] [CrossRef] [Medline]70-Rung
AL, Oral E, Berghammer L, Peters ES. Feasibility and acceptability of a mobile
mindfulness meditation intervention among women: Intervention study. JMIR
Mhealth Uhealth 2020 Jun 02;8(6):e15943 [FREE Full text] [CrossRef]
[Medline]73]. Similar to levels of engagement reported in studies of comparable
mobile mental health programs [Ahtinen A, Mattila E, Välkkynen P, Kaipainen K,
Vanhala T, Ermes M, et al. Mobile mental wellness training for stress
management: Feasibility and design implications based on a one-month field
study. JMIR Mhealth Uhealth 2013 Jul;1(2):e11 [FREE Full text] [CrossRef]
[Medline]46,Deady M, Johnston D, Milne D, Glozier N, Peters D, Calvo R, et al.
Preliminary effectiveness of a smartphone app to reduce depressive symptoms in
the workplace: Feasibility and acceptability study. JMIR Mhealth Uhealth 2018
Dec 04;6(12):e11661 [FREE Full text] [CrossRef] [Medline]70,Lattie E, Cohen KA,
Winquist N, Mohr DC. Examining an app-based mental health self-care program,
IntelliCare for college students: Single-arm pilot study. JMIR Ment Health 2020
Oct 10;7(10):e21075 [FREE Full text] [CrossRef] [Medline]74], participants in
this study engaged with Noom Mood regularly, opening the program approximately
two times per week and performing an action within the app once every 2 to 3
days (11 of 28 days). Participants engaged most with the articles and least with
activities. Of note, it is possible that participants completed activities
offline throughout the week, which is how they were designed, but did not mark
them as complete in the app. As such, it is likely that the data collected on
activities underestimate participant engagement in this aspect of Noom Mood,
given that many activities focus on offline experiences (eg, practicing
breathing exercises or grounding techniques). Future studies will aim to assess
actions completed offline in relationship to symptom outcomes.
ACCEPTABILITY
The vast majority of participants found the program to be logical (92%) and
effective at reducing stress and anxiety (76%). Importantly, 81% of participants
felt confident in recommending the program to a friend. These findings are
similar to other studies of mobile mental health programs and suggest that the
program was perceived to be acceptable to users [Huberty J, Green J, Glissmann
C, Larkey L, Puzia M, Lee C. Efficacy of the mindfulness meditation mobile app
"Calm" to reduce stress among college students: Randomized controlled trial.
JMIR Mhealth Uhealth 2019 Jun 25;7(6):e14273 [FREE Full text] [CrossRef]
[Medline]35,Deady M, Johnston D, Milne D, Glozier N, Peters D, Calvo R, et al.
Preliminary effectiveness of a smartphone app to reduce depressive symptoms in
the workplace: Feasibility and acceptability study. JMIR Mhealth Uhealth 2018
Dec 04;6(12):e11661 [FREE Full text] [CrossRef] [Medline]70,Rung AL, Oral E,
Berghammer L, Peters ES. Feasibility and acceptability of a mobile mindfulness
meditation intervention among women: Intervention study. JMIR Mhealth Uhealth
2020 Jun 02;8(6):e15943 [FREE Full text] [CrossRef] [Medline]73]. Additionally,
at the follow-up assessment, more than half of the participants reported that
they expected that the program would eventually reduce their stress or anxiety
by an additional 50% or more. Future work should investigate long-term outcomes
and whether these participant expectations are borne out.
Participants reported benefiting most from skills training; program features
such as articles, activities, and coaching; learning to better manage their
emotions; and reflective processes such as learning, reflecting, and increasing
their awareness. Participants reported benefiting from taking the time to
reflect on how they were feeling and increasingly becoming aware of their
emotions and thought patterns. Many participants also mentioned benefiting from
the structure and accountability of a designated program. Participants
appreciated the overall tenor of the program; one participant reflected that
“the attitude it strikes is a great balance of cheeky humor but realistic so
it’s not overly strict nor overly cheesy. Makes me connect with it well and
stick with it.” Other participants, however, reported that they hoped for a more
serious tone to the articles. At the time of the study, the program incorporated
jokes and hashtags for the sake of relatability, and has since been modified in
response to participant feedback.
Participants also indicated that the program could be improved to better help
individuals progress in a way that best suits an individual’s idiosyncratic
wants or needs. For example, some participants wanted a slower pace, whereas
others requested more daily reminders. Additionally, some participants provided
feedback that they wanted more specialized interactions with coaches. While
individuals were informed that Noom Mood is not a replacement for therapy and
does not provide clinical assessment or treatment, it is possible that
participants were expecting the coaching feature to function more similarly to
therapy. However, some participants provided feedback stating that responses
given by coaches did not feel personalized and felt too generic. It is also
possible that some participants may not have been good candidates for a
self-help approach. As mentioned previously, in the literature, there is limited
understanding of how participants would experience a commercial mobile mental
health program with personal coaching, rather than therapy. This study
contributes initial understanding that, in this context, coaching can be
helpful, but it can also raise confusion about the role of a coach when
providing guidance and support rather than therapy. Future iterations of the
program should, thus, be sure to set expectations for this feature clearly.
Participants also relayed some suggestions for program improvements that would
provide support in varying environments or situations, such as support for moms
with young children, skills to reduce procrastination, video and audio
recordings, and easily accessible summaries of activities or articles, all of
which should be considered in future programs. Since the time of the study,
audio recordings have been added to the program. Some participants reported that
they would prefer that the program be offered at a lower cost, and some
mentioned they would like the program to be covered by health insurance plans.
In order to increase accessibility, future initiatives and programs should
consider efforts to provide reimbursable experiences (eg, through employee
wellness initiatives).
PRELIMINARY OUTCOMES
ANXIETY SYMPTOMS, PERCEIVED STRESS, AND DEPRESSIVE FEELINGS
From baseline to 4 weeks, anxiety symptoms improved by 36% (d=1.03) in
per-protocol analyses and 23% (d=0.70) in intention-to-treat analyses. In
addition, stress reductions were 21% (d=0.72, per-protocol analysis) and 13%
(d=0.52, intention-to-treat analysis), and depressive feelings decreased by 33%
(d=0.75, per-protocol analysis) and 22% (d=0.55, intention-to-treat analysis).
These effect sizes are comparable to those reported in studies of other mobile
mental health programs with the same study length and outcome measures [Yang E,
Schamber E, Meyer RML, Gold JI. Happier healers: Randomized controlled trial of
mobile mindfulness for stress management. J Altern Complement Med 2018
May;24(5):505-513. [CrossRef] [Medline]44,Bakker D, Kazantzis N, Rickwood D,
Rickard N. A randomized controlled trial of three smartphone apps for enhancing
public mental health. Behav Res Ther 2018 Oct;109:75-83. [CrossRef]
[Medline]75-Hwang WJ, Jo HH. Evaluation of the effectiveness of mobile app-based
stress-management program: A randomized controlled trial. Int J Environ Res
Public Health 2019 Nov 03;16(21):4270 [FREE Full text] [CrossRef] [Medline]80].
Specifically, anxiety and stress decreased in ways that were comparable to or
greater than anxiety reductions shown in previous studies, whereas depression
showed comparable, though smaller, effect sizes [Yang E, Schamber E, Meyer RML,
Gold JI. Happier healers: Randomized controlled trial of mobile mindfulness for
stress management. J Altern Complement Med 2018 May;24(5):505-513. [CrossRef]
[Medline]44,Bakker D, Kazantzis N, Rickwood D, Rickard N. A randomized
controlled trial of three smartphone apps for enhancing public mental health.
Behav Res Ther 2018 Oct;109:75-83. [CrossRef] [Medline]75,Oser M, Wallace ML,
Solano F, Szigethy EM. Guided digital cognitive behavioral program for anxiety
in primary care: Propensity-matched controlled trial. JMIR Ment Health 2019 Apr
04;6(4):e11981 [FREE Full text] [CrossRef] [Medline]78,Flett JAM, Hayne H,
Riordan BC, Thompson LM, Conner TS. Mobile mindfulness meditation: A randomised
controlled trial of the effect of two popular apps on mental health. Mindfulness
2018 Oct 31;10(5):863-876. [CrossRef]79]. Of course, this may reflect the fact
that the program focuses more on stress and anxiety management than on
depression. Of all our outcome measures, anxiety showed the biggest effect
sizes, which contrasts with some studies that have found that anxiety scores did
not improve as much as other symptom measures, such as depression [Bakker D,
Kazantzis N, Rickwood D, Rickard N. A randomized controlled trial of three
smartphone apps for enhancing public mental health. Behav Res Ther 2018
Oct;109:75-83. [CrossRef] [Medline]75,Hwang WJ, Jo HH. Evaluation of the
effectiveness of mobile app-based stress-management program: A randomized
controlled trial. Int J Environ Res Public Health 2019 Nov 03;16(21):4270 [FREE
Full text] [CrossRef] [Medline]80].
EMOTION REGULATION AND OPTIMISM
In this study, we found that emotion regulation improved by 14% (d=0.58,
per-protocol analysis) and 8.6% (d=0.43, intention-to-treat analysis). Emotion
dysregulation is hypothesized to underpin a wide range of psychological
difficulties [Gross JJ, Muñoz RF. Emotion regulation and mental health. Clin
Psychol 1995;2(2):151-164. [CrossRef]81]; in fact, transdiagnostic
interventions, such as DBT or the Unified Protocol [Barlow DH, Harris BA, Eustis
EH, Farchione TJ. The unified protocol for transdiagnostic treatment of
emotional disorders. World Psychiatry 2020 Jun;19(2):245-246 [FREE Full text]
[CrossRef] [Medline]82], focus on emotion dysregulation as the primary treatment
target. Notably, however, emotion regulation is rarely included as an outcome
variable in mobile mental health programs, despite its empirical and theoretical
relevance to mental health and well-being [Eisenstadt M, Liverpool S, Infanti E,
Ciuvat RM, Carlsson C. Mobile apps that promote emotion regulation, positive
mental health, and well-being in the general population: Systematic review and
meta-analysis. JMIR Ment Health 2021 Nov 08;8(11):e31170 [FREE Full text]
[CrossRef] [Medline]19]. In two studies of mHealth programs conducted with young
adults [Dubad M, Elahi F, Marwaha S. The clinical impacts of mobile
mood-monitoring in young people with mental health problems: The MeMO Study.
Front Psychiatry 2021;12:687270 [FREE Full text] [CrossRef] [Medline]83] and
homeless youth [Schueller SM, Glover AC, Rufa AK, Dowdle CL, Gross GD, Karnik
NS, et al. A mobile phone-based intervention to improve mental health among
homeless young adults: Pilot feasibility trial. JMIR Mhealth Uhealth 2019 Jul
02;7(7):e12347 [FREE Full text] [CrossRef] [Medline]84] that measured emotion
regulation as an outcome variable, results showed no significant improvements in
emotion regulation capacity.
We found significantly higher optimism at 4 weeks compared to baseline (15.7% or
d=0.49, per-protocol analysis; 10% or d=0.39, intention-to-treat analysis). To
our knowledge, this is the first mobile mental health study to measure changes
in optimism, though some studies of mobile mental health programs have found
improvements in other positive psychological constructs, such as life
satisfaction, general mental well-being, or quality of life [Ahtinen A, Mattila
E, Välkkynen P, Kaipainen K, Vanhala T, Ermes M, et al. Mobile mental wellness
training for stress management: Feasibility and design implications based on a
one-month field study. JMIR Mhealth Uhealth 2013 Jul;1(2):e11 [FREE Full text]
[CrossRef] [Medline]46,Conversano C, Rotondo A, Lensi E, Della Vista O, Arpone
F, Reda MA. Optimism and its impact on mental and physical well-being. Clin
Pract Epidemiol Ment Health 2010 May 14;6:25-29 [FREE Full text] [CrossRef]
[Medline]85-Weber S, Lorenz C, Hemmings N. Improving stress and positive mental
health at work via an app-based intervention: A large-scale multi-center
randomized control trial. Front Psychol 2019;10:2745 [FREE Full text] [CrossRef]
[Medline]88]. A robust literature base demonstrates that optimism is inversely
correlated with depression and anxiety and positively correlated with measures
of life satisfaction and self-reported health variables [Andersson G. The
benefits of optimism: A meta-analytic review of the life orientation test. Pers
Individ Dif 1996 Nov;21(5):719-725. [CrossRef]89,Forgeard M, Seligman M. Seeing
the glass half full: A review of the causes and consequences of optimism. Prat
Psychol 2012 Jun;18(2):107-120. [CrossRef]90]. Importantly, optimism may
influence physical and mental health by encouraging adaptive coping [Conversano
C, Rotondo A, Lensi E, Della Vista O, Arpone F, Reda MA. Optimism and its impact
on mental and physical well-being. Clin Pract Epidemiol Ment Health 2010 May
14;6:25-29 [FREE Full text] [CrossRef] [Medline]85]. Consistent with previous
findings, both baseline and 4-week optimism scores were significantly negatively
correlated with time-matched anxiety symptoms, stress, and depressive feelings,
and optimism scores were positively correlated with emotion regulation (ie,
higher optimism is correlated with greater capacity to regulate one’s emotions).
Future studies should evaluate optimism and its associations with other mental
health outcomes.
LIMITATIONS
This pilot study had several limitations. First, without a control group, it was
not possible to separate the effects of the program itself from improvement over
time (ie, regression to the mean and maturation). In addition, other
interventions were uncontrolled; that is, program participants may have been
participating in active therapy or may have been taking psychotropic medications
while they were participating in this study. Nevertheless, it is unlikely that
these findings are purely spurious, as the effect sizes are similar to those
found in active treatment groups in RCTs, and they are much larger than those
found in control groups (eg, see Bakker et al [Bakker D, Kazantzis N, Rickwood
D, Rickard N. A randomized controlled trial of three smartphone apps for
enhancing public mental health. Behav Res Ther 2018 Oct;109:75-83. [CrossRef]
[Medline]75]). Now that preliminary feasibility and acceptability have been
established, future studies should use randomized designs to confirm that these
results were due to the program itself. Also, the study was conducted over 4
weeks, and it is unclear whether results would change over longer periods of
time. Further, the study examined the program as a whole, making it difficult to
isolate which specific program components led to changes in outcomes. Future
studies should use causal methods to explore this further. In addition, the
sample was primarily female, White, and highly educated, which is typical of
studies of mobile mental health programs [Eisenstadt M, Liverpool S, Infanti E,
Ciuvat RM, Carlsson C. Mobile apps that promote emotion regulation, positive
mental health, and well-being in the general population: Systematic review and
meta-analysis. JMIR Ment Health 2021 Nov 08;8(11):e31170 [FREE Full text]
[CrossRef] [Medline]19]. Future research should evaluate to what extent these
results would generalize to other populations and actively recruit from
hard-to-reach populations. Lastly, this study did not assess other variables
that may have caused improvement in symptoms, such as psychiatric services,
individual or group therapy, and participants’ use of other self-help materials.
CONCLUSIONS
In this study, we explored the usability, feasibility, acceptability, and
preliminary effectiveness of Noom Mood, a publicly available, mobile mental
well-being program based on CBT and MBSR with personal coaching. The program
follows 11 of Bakker et al’s [Bakker D, Kazantzis N, Rickwood D, Rickard N.
Mental health smartphone apps: Review and evidence-based recommendations for
future developments. JMIR Ment Health 2016 Mar;3(1):e7 [FREE Full text]
[CrossRef] [Medline]5] evidence-based recommendations for mobile mental health
programs: it is based on CBT; addresses both anxiety and low mood; is designed
for use by nonclinical populations; includes reporting of thoughts, feelings,
and behaviors; recommends activities; provides mental health information;
encourages non–technology-based activities; includes gamification or intrinsic
motivation to engage; shows logs of past app use (eg, patterns of logged mood);
uses reminders to engage (eg, messages from the coach); and provides a simple
and intuitive interface and interactions. Our results suggest that Noom Mood was
usable, feasible, and acceptable to participants, with promising preliminary
improvements in anxiety symptoms, stress, depressive feelings, emotion
regulation, and optimism. Future directions should include (1) the incorporation
of changes suggested by participants in this study and (2) more rigorous testing
of outcome variables, such as through randomized designs.
CONFLICTS OF INTEREST
Authors MM, ASH, ESM, CNM, HB, and AM are employees at Noom, Inc, and have
received salary and stock options for their employment. LR received payment from
Noom, Inc, for their role as a consultant on this project and for their
contribution as an author on this paper. LR is also a co-owner of the Triangle
Area Psychology Clinic; a consultant to, and a DBT trainer for, Behavioral Tech,
LLC; and an employee of University of North Carolina School of Medicine. There
are no specific conflicts to report with those entities, and none of those
entities were involved in their contribution to this project.
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--------------------------------------------------------------------------------
ABBREVIATIONS
ACT: acceptance and commitment therapyCBT: cognitive behavioral therapyCEQ:
Credibility and Expectancy QuestionnaireDBT: dialectical behavior
therapyDERS-SF: Difficulties in Emotion Regulation Scale–Short FormGAD-7: 7-item
Generalized Anxiety Disorder scaleLDA: latent Dirichlet allocationLOT-R: Life
Orientation Test–RevisedMBSR: mindfulness-based stress reductionmHealth: mobile
healthPHQ-8: 8-item Patient Health Questionnaire depression scalePSS-4: 4-item
Perceived Stress ScaleRCT: randomized controlled trialSUS: System Usability
Scale
--------------------------------------------------------------------------------
Edited by A Mavragani; submitted 26.01.22; peer-reviewed by M EIsenstadt, A
Teles; comments to author 25.02.22; revised version received 08.03.22; accepted
23.03.22; published 15.04.22
Copyright
©Meaghan McCallum, Annabell Suh Ho, Ellen Siobhan Mitchell, Christine N May,
Heather Behr, Lorie Ritschel, Kirk Mochrie, Andreas Michaelides. Originally
published in JMIR Formative Research (https://formative.jmir.org), 15.04.2022.
This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, 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 this copyright and license
information must be included.
CITATION
Please cite as:
McCallum M, Ho AS, Mitchell ES, May CN, Behr H, Ritschel L, Mochrie K,
Michaelides A
Feasibility, Acceptability, and Preliminary Outcomes of a Cognitive Behavioral
Therapy–Based Mobile Mental Well-being Program (Noom Mood): Single-Arm
Prospective Cohort Study
JMIR Form Res 2022;6(4):e36794
doi: 10.2196/36794 PMID: 35436218 PMCID: 9055471
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THIS PAPER IS IN THE FOLLOWING E-COLLECTION/THEME ISSUE:
Formative Evaluation of Digital Health Interventions (1401) mHealth for
Wellness, Behavior Change and Prevention (2216) Anxiety and Stress Disorders
(792) Users' and Patients' Needs for Mental Health Services (287)
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