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Targeting the Needs of Self-Determination Theory: An Overview of Mental Health Care Apps

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Introduction: Smartphone apps are a highly accessible tool to supplement self-treatment for mental health challenges, such as depression, and are underrepresented in research. While many studies have performed content analyses of health apps, few studies have reviewed their adherence to behavior theory. Aims: The objective of this study is to assess mHealth depression apps through the lens of the Self-Determination Theory and identify if app functions target the three basic needs: autonomy, competence, and relatedness. Methods: All depression apps available from iTunes and Google Play that met inclusion criteria were analyzed (N = 194). Apps were reviewed for price options, store availability, download rates, and how functions targeted the three basic needs for intrinsic and sustained health behavior change outlined in the Self-Determination Theory. Results: Findings showed that most of the apps targeted at least one of the basic needs (158/194, 81.4%). However, only a few of these apps targeted all three basic needs to some degree (15/194, 7.7%), and no single app targeted all three basic needs fully. Furthermore, neither store availability, price option nor download rates were accurate predictors that apps targeted the three basic needs. Conclusions: The results suggest that some depression apps targeted autonomy, competence, and relatedness but this was limited to a small number of apps through few functions available in each app. People who want access to more functions targeting the needs would need to download a suite of apps.
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Introduction
e number of mHealth apps continues to grow and is predicted to be over 325,000 (Larson, 2018). While there
are some eorts to provide consumers with guidance on which apps have an evidence base (e.g., NHS App Library;
McCartney, 2013), overall, governmental regulators do not recognize mHealth apps as medical devices and impose
no authority over dissemination. is means that developers can develop and distribute apps through apps stores
as treatment tools for depression without regulation. is leaves consumers vulnerable to poor quality health apps.
However, due to the benets of mobile apps as a platform to supplement self-treatment of depression, many apps
have been developed and are competing to capture the market transitioning to the mHealth platform. e aord-
ability of mHealth apps, coupled with the accessibility, practicality, and expectations of privacy, provides a viable
healthcare option for private and stigmatized health concerns (Deng et al., 2014). As a result, this makes mHealth
apps a viable option for people with depression. However, most of these apps are not developed by health profes-
sionals (Powell et al., 2016), and there are cases of apps misreporting organizational aliations and content (Shen
Targeting the Needs of Self-Determination eory:
An Overview of Mental Health Care Apps
Luke BROWNLOW 1
Introduction: Smartphone apps are a highly accessible tool to supplement
self-treatment for mental health challenges, such as depression, and are
underrepresented in research. While many studies have performed content
analyses of health apps, few studies have reviewed their adherence to behavior
theory.
Aims: e objective of this study is to assess mHealth depression apps through
the lens of the Self-Determination eory and identify if app functions
target the three basic needs: autonomy, competence, and relatedness.
Methods: All depression apps available from iTunes and Google Play that
met inclusion criteria were analyzed (N = 194). Apps were reviewed for
price options, store availability, download rates, and how functions targeted
the three basic needs for intrinsic and sustained health behavior change
outlined in the Self-Determination eory.
Results: Findings showed that most of the apps targeted at least one of the
basic needs (158/194, 81.4%). However, only a few of these apps targeted
all three basic needs to some degree (15/194, 7.7%), and no single app
targeted all three basic needs fully. Furthermore, neither store availability,
price option nor download rates were accurate predictors that apps targeted
the three basic needs.
Conclusions: e results suggest that some depression apps targeted autonomy,
competence, and relatedness but this was limited to a small number of apps
through few functions available in each app. People who want access to more
functions targeting the needs would need to download a suite of apps.
Keywords: mHealth, depression, mobile applications, the Self-Determination
eory, SDT
OPEN ACCESS
1 College of Business, Government and Law,
Flinders University, Adelaide, Australia
Correspondence
Luke Brownlow
College of Business, Government and Law, Flinders
University
Postal Address: Flinders University, Sturt Road,
Bedford Park, SA 5042, Adelaide, Australia
Email: luke.brownlow@inders.edu.au
History
Received: 6 July 2021
Accepted: 5 February 2022
Citation
Brownlow, L. (2022). Targeting the needs of
self-determination theory: An overview of mental
health care apps.
European Journal of Mental Health, 17(1), 91–100.
https://doi.org/10.5708/EJMH/17.2022.1.8
REVIEW ARTICLE
ISSN1788-7119 (online)
© 2022 The Authors. Published by Semmelweis University Institute of Mental Health, Budapest ejmh.eu
European Journal of Mental Health
https://doi.org/10.5708/EJMH/17.2022.1.8
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 92
et al., 2015). ese apps need to demonstrate some level of clinical foundation to be safe and eective (Powell et
al., 2016), yet wide-spread dissemination means there are unfounded apps in circulation and non-specialist apps
masking themselves as depression apps (Shen et al., 2015). Additionally, these apps could have adverse eects and
worsen depression (Leigh & Flatt, 2015). Newspaper reports have claimed that most mHealth depression apps
are unproven (Knapton, 2015) and many apps foster depression and depressive symptoms and do not oer any
eective treatment (Ryu, 2012). Without guidance, nding a research-based mHealth depression app would be
challenging (Shen et al., 2015), particularly as few mHealth depression apps incorporate health behavior theory
into their designs (Sama et al., 2014). is study hopes to oer suggestions for suitable apps with a human behav-
ior theory basis. mHealth has strong potential for mental health intervention yet more formal assessments around
mHealth are needed (Grossman et al., 2020). is study exists to review mHealth depression apps for in-app
functions that operate to target basic needs for health behavior change outlined by the Self-Determination eory
(SDT) (Ryan et al., 2008). An SDT-based approach to treatment of depression is empirically supported (Britton et
al., 2008) and is well-suited to the app platform (Umaefulam & Premkumar, 2020). Furthermore, it is important
for us to conduct this type of research around enabling change in a context of depression due to the barriers to
health behavior change that depression enacts (Williams, 2014). Despite this, there is minimal research around
SDT treatment approaches to mental health issues through apps (Fish & Saul, 2019).
e Self-Determination eory
is study assesses mHealth depression apps using SDT as a theoretical basis. SDT acknowledges the fulllment of
three basic needs: autonomy, competence, and relatedness. ese needs are crucial to create and support intrinsic,
long-term motivation for behavioral change, and a lack of needs satisfaction can result in depression (Bartholomew
et al., 2011) and depressive symptoms (Ryan et al., 2008) Furthermore, controlled motivation, as opposed to in-
trinsic motivation, can be associated with depression (Levesque et al., 2007). In light of this, the sample of apps
in this study are reviewed for functions that target the three basic needs congured by SDT. Other popular health
behavior theories, including the Health Belief Model and the eory of Reasoned Action, are limited in their abil-
ity to explain health behavior change (Rothman, 2000). Conversely, SDT oers keen insight into understanding
health behavior through apps (Monney et al., 2015). and depression treatment (Ryan & Deci, 2008).
Autonomy
e need for autonomy is the need for self-direction of behavior that is motivated by value, satisfaction, and inter-
est in the behavior to the exclusion of external stimuli (Ryan et al., 2008). It is self-management of health with
limited to no support from others (Ryan et al., 2008). In SDT, the motivation to change behavior is dependent
on autonomy; when motivation is autonomous, people are more willing to sustain health behavior change than
when motivation is forced (Ryan et al., 2008). Furthermore, a lack of autonomy in the face of dicult situations
can be an indicator of depressive onset (Mazure et al., 2000), and low autonomy levels are associated with de-
pressive symptoms (Schirin et al., 2019). is suggests that autonomy absence is a trigger for issues concerning
mental health. Fostering health behavior change through supported autonomy can be identied as follows. Firstly,
providing meaningful reasons for behavior change. Secondly, providing alternative behaviors or activities to en-
act that require active participation. irdly, supporting individual initiatives through personalization. Fourthly,
recognizing alternative opinions or approaches to content. In light of this, it is assumed that mHealth depression
apps with these qualities would support depression self-treatment.
Competence
e need for competence is the need to feel adept or skillful towards a behavior or the feeling that a skill or be-
havior change is improving (Ryan et al., 2008). It is dierent to autonomy, as it reects personal ability rather
than personal control. Experiencing the feeling that you are capable of changing a behavior is a component of
motivation and maintenance of long-term behavior change. Furthermore, lower levels of competence are associ-
ated with depressive symptoms (Schirin et al., 2019). is supports ndings that self-ecacy and competence
foster eective depression treatment (Ryan & Deci, 2008). Feelings of competence come from tools, advice, and
feedback that encourage behavioral changes and overcome barriers (Ryan et al., 2008). In light of this, apps that
foster competence for depression self-treatment would provide sources of information that support depression
treatment. Additionally, apps would include supportive tools that enable change and feedback mechanisms.
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 93
Relatedness
e need for relatedness is the need to feel connected to others (Ryan et al., 2008). In SDT, feelings of relatedness
are important for establishing motivation as it allows people to adopt social inuences from meaningful relation-
ships. Social inuence is an important component to health behavior change (Fogel et al., 2002), including treat-
ing depression (Logsdon et al., 2009). Furthermore, relatedness and social inuence are increasingly important
aspects of our digital lives and online presence. is is also true for our digitization of health behavior and man-
agement. For instance, online communities, forums, and chat rooms enable social engagement with likeminded
people with relatable health issues (Fogel et al., 2002). mHealth apps that enable social connection with peers and
feelings of relatedness would likely contribute to positive health behavior change around depression.
To sum up, in accordance with SDT, the needs for autonomy, competence, and relatedness are central to innate
and sustained motivation to make behavioral changes. mHealth depression apps that recognize these needs are
likely to make greater positive changes for people with depression than those apps which overlook these needs and
other aspects of behavior and treatment theories. In light of this, this study investigates the following question:
Do mHealth depression apps host functions to target user needs for autonomy, competence, and relatedness in
line with SDT? From this investigation, the study makes suggestions of mHealth depression apps.
Methods
Sample Selection
For this app review study, mHealth depression apps are recognized as apps that claim to treat, manage, educate,
or help with depression and depressive symptoms. Generic mental health treatment apps were included only
if they met this criterion. Apps focusing on symptom assessment and mood monitoring were excluded. With
granted ethics clearance, two popular app stores (Google Play and Apple iTunes) were checked for any apps using
the words “depression” and “depressive” in either the titles, keywords or descriptions of their app store webpages
during March 2020. All search results from both stores were reviewed to assess if the apps qualied for study
inclusion. A range of specialist apps were reviewed, including, but not limited to, chat apps, workbook apps,
mindfulness-based apps and faith-based apps. Several apps, for instance hypnosis and acupuncture apps, were
excluded because they cannot be appropriately analyzed within the context of this research. Social chat and com-
munication apps were also excluded as they are generic communication apps without a focus on depression. Apps
that were not in English were also excluded. From this sampling approach, 221 apps were reviewed: 144 from
Google Play and 77 from iTunes. After correcting for twenty-seven duplicates, the sample was reduced to 194. As
developers will occasionally give an app dierent names across app stores, both app names and developer names
were recorded during data collection. By sorting the original 221 apps by app names, then developer names, all
duplicates were identied by comparing descriptions, imagery and content. Any apparent duplicate apps were
compared between the two app stores concurrently for conrmation.
Coding of Apps
App and Download Details
Apps were coded for market type (Google Play, iTunes, or both stores) and price type (free or paid). Download
rates were included for apps available from Google Play only as iTunes does not report download information.
Apps were reviewed according to titles, descriptions, and imagery; a supported method of data collection
in mHealth research (Pinheiro et al., 2019; Wali et al., 2019). e coding scheme was developed by using
the conceptualization of the SDT variables outlined earlier. As a quality check, 20% of included apps were
randomly selected and recoded by the author to conrm consistency in the coding. Functions that targeted
the three basic needs were observed and coded appropriately. Some apps gave instructions that align with
SDT; however, they did not provide functions to support it and were coded appropriately. For example, some
apps advised users to make to-do lists while others hosted to-do list functions which enable the behavior and
target the need for competence. Additionally, to target the need for autonomy, an app must provide in-depth,
objective information about depression (e.g., psychoeducation approach, Harrer et al., 2021) as opposed to a
brief, undiscerning description.
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 94
Autonomy
Autonomy was assessed through functions in apps that supported autonomous behavior of users through: 1)
providing meaningful reasons for change, 2) providing alternative behaviors, and 3) supporting individual initia-
tives through personalization. Coding was based on the presence or absence of these functions in the apps. First,
apps were assessed for relevant, sound information that supports reasons for behavior change, such as objective
and clinical information on depression and depressive symptoms and valid reasons for treatment. Valid reasons
are justications that treating depression improves health and life quality. e second aspect of autonomy con-
siders choices and behavior alternatives. Apps were reviewed for activities and exercises with active participation
that redirect concentration towards treatment, such as workbooks, guided meditation, writing, learning games,
and mindfulness practices. Apps with a dedicated treatment approach, such as mindfulness and CBT exercises,
likely have a strong overlap with elements of SDT. e third aspect of autonomy is to support individual initia-
tives. Here, apps were reviewed for functions that enable change through behavior plans or schedules, such as a
personalized depression management plan outlining how to act and react for future situations and how to achieve
behavior goals. e last aspect of autonomy considers individual perspectives. is can only be assessed in inter-
personal communication. erefore, this aspect of autonomy was not coded for this study.
Competence
Competence is enabled by skill development, supportive tools for development and feedback on behavior change
(Ryan et al., 2008). Coding was dependent on whether apps hosted functions that target the need for competence
through: 1) resources, 2) supporting change, and 3) providing feedback. Firstly, apps were reviewed for informa-
tional resources on skills for change, such as treatment guidelines and insights and stories from lived experience.
Practical resources to educate app users, such as FAQs, hotlines, educational quizzes, and games were also identi-
ed. e second aspect of competence can be seen in app functions that actively support change, such as remind-
ers and push notications, goal settings and to-do list functions, and mood assessments and journaling. Finally,
apps were reviewed for evidence of providing feedback, such as personalized reviews of progress based on mood
and behavior tracking and journaling.
Relatedness
Relatedness is determined by feelings of connectedness to peers (Ryan et al., 2008). erefore, apps were reviewed
for relatedness by observing functions that connect users with peers and chatbots to enable social support and
with clinicians to enable more formal correspondence. Functions such as chat rooms, forums, bulletin boards,
messaging functions, and connection to social media were identied as evidence of mHealth depression apps sup-
porting relatedness and coded appropriately.
Results
App and Download Details
Out of 194 apps, the majority were uniquely available to Google Play (118/194, 60.8%) while around one-quar-
ter (50/194, 25.8%) were exclusive to iTunes. As a result, a minority of apps were available from both app stores
(26/194, 13.4%). In terms of price type, 135 of the 194 apps reviewed (69.6%) were free, and the remaining apps
(59/194, 30.4%) required some form of payment. Price type did not predict download rates in Google Play as the
highly downloaded apps were a combination of free and paid. iTunes does not disclose download rates for apps
meaning download rate could not be used either. Google Play download rates are provided as approximations,
so no exact numbers can be given in this study. However, based on these approximations, it is clear that all 144
mHealth depression apps from Google Play have been downloaded at least once. One million downloads was the
highest rate which was represented by eight apps. Approximately 80,000 was the average rate despite the majority
of apps having low rates and the popular minority, less than twenty percent, having more downloads than the
average. However, download rates were not a reliable indicator that apps targeted the basic needs of SDT as highly
downloaded apps did not appear to consistently meet the basic needs.
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 95
Functions Satisfying the Basic Needs
is study aims to investigate whether mHealth depression apps, through their functions, satised needs for
autonomy, relatedness, and competence as characterized by SDT. Analysis showed that while no apps hosted all
functions identied in the coding criteria, most apps (158/194, 81.4%) hosted a function that satised at least
one need, and only fteen apps (15/194, 7.7%) hosted functions that targeted all three basic needs. However,
thirty-six apps (19%) did not host any functions outlined, meaning that they did not target any of the basic needs.
Just over one-third of the apps satised the need for autonomy (73/194, 37.6%). Four app functions were ob-
served in this study to target the need for autonomy. First, providing information on depression (36/194, 18.6%).
Second, activities that involve active participation (42/194, 21.6%). ird, assisting with creating a personalized
depression management plan (6/194, 3.1%). Lastly, no apps provided reasons for treating depression. Almost
half of the apps targeted the need for competence (95/194, 49%). Few apps provided information resources
on skills for change towards competence; ve apps (2.6%) oered treatment guidelines or tips, and twelve apps
(6.2%) provided practical resources for education on depression and treatment. e other functions that target
the need for competence are as follows. e two functions, 1) reminders or notications and 2) goal setting or
to-do lists, were each seen in nine apps (4.6%) while mood tracking or journaling functions were found in close
to half (84/194, 43.3%). Functions that gave feedback, including personalized reviews of progress and behavior
change, were observed in thirty-one apps (16%). e need for relatedness was addressed by one-quarter of the
apps (49/194, 25.3%) making it the least-targeted need outlined by SDT. e most common function that tar-
geted this need allowed users to communicate with peers, clinicians, and even chatbots. Five apps (2.6%) hosted
a function for social media connection that allowed users to link their app behavior to social media.
Table 1. Apps Functions Corresponding to the Needs of the Self-Determination eory (N=194)
e Self-Determination eory Needs through Functions Distribution, n (%)
Autonomy 73 (37.6)
Meaningful Reasons for Behavior Change 36 (18.6)
Function - Information on Depression 36 (18.6)
Function - Reasons for Treating Depression 0 (0)
Providing Choices and Behavior Alternatives 42 (21.6)
Function - Activities that Involve Active Participation 42 (21.6)
Supporting Individual Initiatives 6 (3.1)
Function - Personalized Depression Management Plan 6 (3.1)
Competence 95 (49)
Information Resources on Skills for Change 17 (8.8)
Function - Treatment Guidelines or Tips 5 (2.6)
Function - Practical Resources for Education 12 (6.2)
Supportive Tools for Behavior Change 88 (45.4)
Function - Reminders or Notifications 9 (4.6)
Function - Goal Setting or To-Do List 9 (4.6)
Function - Mood Tracker or Journaling 84 (43.3)
Feedback 31 (16)
Function - Personalized Review of Progress or Change 31 (16)
Relatedness 49 (25.3)
Interaction with Others 49 (25.3)
Function - Connection with Peers, Chatbots or Clinicians 47 (24.2)
Function - Connection with Social Media 5 (2.6)
Note. e distribution does not add up in cases where apps have multiple functions within a single need.
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 96
Suggested Apps Based on Popularity and Basic Needs
Of the three most downloaded mHealth depression apps, two apps, Youper (Youper, 2020) (Figure 1) and Mood-
path (ReachOut Australia, 2020) (Figure 2), targeted the needs for autonomy, competence, and relatedness through
ve functions, the highest number of observed functions in any app in this study. Furthermore, both of these apps
were free and available in both app stores making them an attractive starting point for anyone seeking apps for de-
pression self-treatment. Youper is an articial intelligence app where users can connect with a chatbot designed to
use empathetic language while they complete therapy exercises on their device. Moodpath describes itself as a clini-
cal app. It provides a mood journal for users and analyzes their health assessments to develop individualized progress
reports and suggestions for self-reections and activities. Based on how these apps targeted the needs for autonomy,
competence, and relatedness, their stand-out concepts and aesthetics, and their popularity based on download rates,
this study suggests two particular apps, Youper and Moodpath, as a starting point for anyone seeking an mHealth
depression app. Both apps scored highly in a study conducting quality evaluation of mHealth depression apps and
each have produced positive results in clinical trials (Burchert et al., 2021; Mehta et al., 2021).
Discussion
Main Findings
Using SDT as a theoretical basis, this study reviewed mHealth depression apps (N = 194) available from the
Google Play and iTunes app stores. Research into applications of SDT to mHealth apps appears to be minimal
(see Eysenbach et al., 2020). Hence, the objective in this study was to identify functions in apps that target the
needs for autonomy, competence, and relatedness to identify apps with a SDT basis. ese needs are outlined by
SDT as crucial for creating and supporting intrinsic, long-term motivation for health behavior change (Ryan et
al., 2008). e analysis proposes that the minority of the apps hosted features to target the basic needs outlined by
SDT. In particular, one-quarter (49/194, 25.3%) of the apps satised the need for relatedness, and less than half
targeted autonomy (73/194, 37.6%) and competence (95/194, 49%). is suggests that nding an app for self-
treatment of depression that targets the needs would not be immediate for people using Google Play or iTunes.
Furthermore, neither the price type nor download rates are reliable indicators; apps that targeted the three basic
Figure 1. Youper
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 97
needs were reected similarly in both paid (91/194, 46.9%) and free apps (103/194, 53.1%) and had varying
download rates.
e study found that while a small portion of the apps (36/194, 19%) did not target any of the basic needs, a
smaller portion (15/194, 7.7%) hosted functions that targeted all three basic needs. Research demonstrates that
addressing the three basic needs of SDT is a prerequisite to building and maintaining intrinsic motivation for
behavioral change (Ryan et al., 2008). In light of this, it can be argued that mHealth depression apps that do not
target the three basic needs would produce limited results. In contrast, the few apps that target all three needs
might foster motivation and positive, long-term health behavior change. Although few of the apps targeted all
three needs, people are able to build intrinsic motivation for behavior change by targeting the three basic needs
by using a single mHealth depression app. erefore, individuals would not need to access a suite of apps which
would be a more costly and less practical approach. Despite this, the eectiveness of mHealth depression apps
that target all three basic needs would depend correspondingly on other factors not covered here, including but
not limited to, users’ subjective evaluation of app engagement techniques (i.e., gamication and aesthetics) and
technology acceptance and usage drivers (i.e., perceived ease of use and usefulness).
Strengths and Limitations
e strength of this study is that it presents a theory-driven overview of mHealth depression apps that are avail-
able and highly accessible in the market and is bolstered with empirical analysis. is study also has limitations.
Firstly, the apps reviewed were limited to Google Play and iTunes. Consequently, other relevant apps available
only in other competing app stores, e.g., Amazon Appstore and Windows Appstore, may likely have been ex-
cluded. Furthermore, apps reviewed in this study were limited to apps with content delivered in English further
truncating the selection of mHealth depression apps. Secondly, as iTunes does not disclose download rates, all
analyses and discussion around download rates are based solely on the information provided by Google Play.
irdly, by focusing solely on SDT, this study has overlooked many other behavior change theories and therapies,
such as operant conditioning theory and cognitive behavior therapy, that may have more relevance to mHealth
and mental health management spaces.
Figure 2. Moodpath
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 98
Conclusion, Implications and Future Directions
To summarize, this study reviewed mHealth depression apps to identify functions that target the basic needs for
autonomy, competence, and relatedness which foster intrinsic motivation for behavioral change as outlined in
SDT. To some extent, the apps that were reviewed targeted these needs and may be useful. Nonetheless, most of the
apps oered limited functions to satisfy these needs and few apps targeted all three needs. is corresponds with
other ndings that mental health support apps are not at an acceptable standard (Larsen et al., 2016). e study
reviewed all mHealth depression apps available from the Google Play and Apple iTunes app stores that claimed to
treat, support, manage, or help people with depression and depressive symptoms. With this in mind, it is suggested
that signicant potential remains for improvement to these apps by rening and introducing functions guided by
health behavior theory. However, the ndings from the study coupled with the accessibility of apps makes mHealth
depression apps, at the very least, an ideal platform to begin treatment. Of course, experimental research is needed
to measure the ecacy of mHealth depression apps.
While this research is not assessing the eectiveness of mHealth depression apps based on user outcomes, it oers
a review. ere are some implications to this. Firstly, although some apps may target the three basic needs, it is pos-
sible that these apps are limited in their ability to engender sustained behavioral change as this rudimentary research
overlooks many other relevant factors. Secondly, health professionals should consider a suite of studies on the topic
and make their own assessment before prescribing mHealth depression apps to patients. irdly, users of these apps
may not have the capacity to access or use academic literature on the topic, so future research should investigate how
to best give people the skills and materials to make their own assessment of mHealth depression apps. Furthermore,
future research that clinically assesses whether these apps and functions target needs of depressive individuals would
be a contribution to the eld. Lastly, it is unknown how many healthcare-developed apps are oered or in use today,
and there is limited research on treatment of depression through apps and how apps can be used safely without the
support of a clinician. Also, why people turn to apps and whether the healthcare system encourages this movement
is underrepresented in research.
Funding
is research received no specic funding from any agency, commercial or not-for-prot sectors.
Author contribution
Luke Brownlow: conceptualization, design, methodology, investigation, project administration, data manage-
ment, formal analysis, interpretation, writing original draft, writing review and editing.
e author gave nal approval of the version to be published and agreed to be accountable for all aspects of the
work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately
investigated and resolved.
Declaration of interest statement
e author has no conicts of interest to disclose.
Ethical statement
is manuscript is the author’s original work.
Human participants have been not involved in this study.
No ethical approvement, informed consent or data handling policy was needed.
ORCID
Luke BROWNLOW https://orcid.org/0000-0003-2970-9331
L. BROWNLOW An Overview of Mental Health Care Apps
Eur. J. Ment. Health 2022, 17(1), 91–100 99
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... One of the main concerns is that many apps are not developed by health professionals, and they can have adverse effects on the mental health of customers. Due to the absence of governmental regulations, developers can create and distribute them without oversight [12]. Furthermore, many mHealth tools lack evidencebased support, and trial findings are often not replicated over time to validate the results [7]. ...
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Objectives We examined gender differences in helicopter parenting and emerging adults’ well-being through the basic psychological needs of autonomy, competence, and relatedness. Based on gender congruence theory, we hypothesized that daughters’ well-being would be more adversely impacted by their mothers’ helicopter parenting compared to fathers’, while the opposite pattern would emerge for sons. Method Participants were 446 college students between 18–25 years old who completed an online survey. The majority of participants were white, female, underclassman from middle to upper-middle class families. Results Participants reported that their mothers engaged in more helicopter parenting than their fathers. Male and female participants did not differ in the amount of helicopter parenting they experienced, so we tested a model combining these sub-samples. Two minor differences were identified: Daughters reported maternal helicopter parenting was more strongly associated with decreased autonomy and sons reported paternal helicopter parenting was more strongly associated with a decreased relatedness. Thus, a partial equivalence model was tested with only these two paths free to vary between groups. Maternal helicopter parenting was indirectly associated with their children’s reduced well-being on all three measures (i.e., anxiety, depression, and satisfaction with life) through a reduced sense of autonomy and competence. Paternal helicopter parenting was only indirectly associated with their offspring’s well-being through autonomy. Conclusions Results supported prior research suggesting helicopter parenting adversely affects emerging adults’ well-being through its negative impact on the basic psychological needs of self-determination. There was limited support for gender differences in the impact of helicopter parenting on emerging adults.
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Mobile technology is increasingly being used to enhance health and wellness, including in the assessment and treatment of psychiatric disorders. Such applications have been referred to collectively as mHealth, and this article provides a comprehensive review and clinical perspective of research regarding mHealth in late-life mood and anxiety disorders. The novel data collection offered by mHealth has contributed to a broader understanding of psychopathology, to an increased diversity of psychological interventions, and to novel methods of assessment that may ultimately provide individually adaptive mental health care for this population. Older adults face challenges (e.g., transportation, mobility) that limit their ability to receive medical and mental health care services, and mHealth may improve the capacity to reach this population. Although several mobile interventions exist for health-related issues in older adults (e.g., balance, diabetes, medication management), mHealth targeting psychiatric disorders is limited and most often focuses on problems related to dementia, cognitive dysfunction, and memory loss. Given that depression and anxiety are two of the most common mental health concerns among this population, mHealth has strong potential for broad public health interventions that may improve effectiveness of mental health care via individualized assessments and treatments.
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Background: Diabetes is increasingly widespread among Indigenous people, and diabetic retinopathy (DR) is a diabetes eye complication and a common cause of vision loss among adults in Canada. Indigenous women have a high risk of diabetes which increases their risk for DR. This study explored utilizing mobile health (mHealth) via text messages to provide DR awareness and improve diabetic-eye care behavior. This study identified the changes in DR awareness and eye care behavior due to a mHealth education intervention among Indigenous women with or at risk of diabetes. Methods: A pre-post study which adopted an embedded concurrent mixed methods approach guided by self-determination theory and the medicine wheel. Study participants were First Nations and Métis women living with or at risk of diabetes in Saskatoon, Canada. Data was collected via sharing circles and a DR knowledge, attitude, and practice survey. Pre-intervention participants' baseline information on DR knowledge and behavior were obtained from participants. After that, participants received daily text messages on diabetes-eye related information for 12 weeks. Post-intervention, the impact of the mHealth intervention on DR awareness and eye care behavior was assessed. Results: Following the intervention, the DR knowledge, attitude, and practice scores significantly improved. Individuals living with diabetes had increased DR attitude and practice post-scores compared to those at risk of diabetes. Older women had a lower pre-post change in practice scores compared to younger women. The mHealth intervention provided a holistic approach to support diabetes-eye care and empowered the study participants to eat healthily, take medication as prescribed, and have regular medical and eye check-ups. Conclusions: The mHealth education intervention increased DR awareness and fostered a change in diabetes-eye care behavior. Health information via text messaging can motivate, provide support, and empower individuals as well as prevent and manage chronic conditions and reduce the risk of complications.