A systematic quality rating of available mobile health apps for borderline personality disorder [original]

Steubletal.

Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

https://doi.org/10.1186/s40479-022-00186-w

RESEARCH ARTICLE

A systematic quality rating ofavailable

mobile health apps forborderline personality

disorder

Lena Sophia Steubl1* , Josephin Reimann1, Laura Simon1, Yannik Terhorst1, Michael Stach2,

Harald Baumeister1, Lasse Bosse Sander3 and Eva‑Maria Messner1

Abstract

Background: Mobile health apps (MHAs) may offer a mean to overcome treatment barriers in Borderline Personality

Disorder (BPD) mental health care. However, MHAs for BPD on the market lack transparency and quality assessment.

Methods: European app stores were systematically searched, and two independent trained reviewers extracted

relevant MHAs. Employed methods and privacy and security details documentation of included MHAs were extracted.

MHAs were then assessed and rated using the German version of the standardized Mobile Application Rating Scale

(MARS‑G). Mean values and standard deviations of all subscales (engagement, functionality, aesthetics, information,

and therapeutic gain) and correlations with user ratings were calculated.

Results: Of 2977 identified MHAs, 16 were included, showing average quality across the four main subscales

(M = 3.25, SD = 0.68). Shortcomings were observed with regard to engagement (M = 2.87, SD = 0.99), potential

therapeutic gain (M = 2.67, SD = 0.83), existing evidence base (25.0% of included MHAs were tested empirically), and

documented privacy and security details. No significant correlations were found between user ratings and the overall

total score of the MARS‑G or MARS‑G main subscales.

Conclusions: Available MHAs for BPD vary in quality and evidence on their efficacy, effectiveness, and possible

adverse events is scarce. More substantial efforts to ensure the quality of MHAs available for patients and a focus on

transparency, particularly regarding privacy and security documentation, are necessary.

Keywords: BPD, Internet‑ and mobile‑based interventions, IMIs, Treatment, Quality rating

permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the

original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or

other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line

to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory

regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this

licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco

mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Background

Borderline Personality Disorder (BPD) is the most com-

mon personality disorder in clinical settings, with a

prevalence rate of up to 20% among psychiatric inpa-

tients [1]. The prevalence rate in the general population

is estimated to be 1-2% [1]. While there is a wide range of

effective treatment approaches (e.g., dialectical behavior

therapy, DBT; mentalization-based therapy, MBT; [2]),

only a limited number of those affected receive evi-

denced-based treatment [3]. This may partly occur due

to a lack of trained mental health care professionals and

limited budgets [4, 5].

Compared to traditional face-to-face settings, Inter-

net- and mobile-based interventions (IMIs) offer various

advantages that might help overcome potential treat-

ment barriers (e.g., scalability, anonymity, flexibility;

[6, 7]). Mobile health apps (MHAs), in particular, offer

users the opportunity to flexibly integrate treatment into

their daily lives, given their status as ubiquitous devices.

Open Access

*Correspondence: lena.steubl@uni‑ulm.de

1 Institute of Psychology and Education, Department of Clinical Psychology

and Psychotherapy, Ulm University, Ulm, Germany

Full list of author information is available at the end of the article

Page 2 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

Consequently, it may be argued that the treatment gap

for BPD could be reduced with the help of MHAs that are

already available.

Several meta-analyses showed the efficacy of IMIs for

various mental disorders (e.g., [8–10]), as yet mostly

based on Internet-based interventions, while evidence

for MHAs is less established [11]. Correspondingly, a

recent scoping review on technology-based interventions

for BPD shows that while evidence for Internet-based

interventions is emerging, there is only little evidence for

MHAs for BPD [12]. However, results on all five included

MHAs supported their acceptance and/or usability [12].

A recent systematic review and meta-analysis on seven

randomized controlled trials (RCTs) of MHAs for adults

with common BPD symptoms [13] showed no significant

effects between conditions with and without (e.g., wait-

list, treatment-as-usual) MHAs on BPD-related symp-

toms. Yet, the number of included studies was rather

small, only half of the MHAs included in the systematic

review and meta-analysis were commercially available,

and there is a wide range of MHAs on the market not

tested empirically [13].

Regarding MHAs available on the market, prior

research has shown that user ratings are strongly influ-

enced by user-friendliness and are therefore often mis-

leading regarding clinical app quality, while they may be

the only source of information on the quality for poten-

tial users (e.g., [14–16]). This is especially important

considering the fact that the use of MHAs can also come

along with risks and side effects (e.g., insufficient data

protection, treatment without informed consent, criti-

cally wrong information, inadequate handling of crises

[17]).

Therefore, the objective of this study is to investigate

MHAs for BPD already available on the market and

thereby go beyond previous reviews on published effi-

cacy and effectiveness trials. The aim is to (a) examine the

content and evidence of MHAs for BPD and (b) assess

the quality of the MHAs for BPD with regard to engage-

ment, functionality, aesthetics, information, and poten-

tial therapeutic gain.

Methods

Search strategy

MHAs available in German and British app stores (i.e.,

Apple App Store for iOS MHAs and Google Play Store

for Android MHAs) were systematically searched with

BPD-related English, German, and French search terms

(see Additional file1) employing a web crawler [18]. The

validity of this procedure has been proven in previous

studies (e.g., [19, 20]). All terms were searched individu-

ally as logical operations and truncation cannot be used

in the app stores. The search was conducted in Septem-

ber 2020.

Inclusion andexclusion criteria

The inclusion process was divided into two parts and

performed by two reviewers with a degree in psychol-

ogy (JR, JW) under the supervision of a researcher with a

Master’s degree in psychology and in training to become

a licensed psychotherapist (LSS). First, all identified

MHAs were screened, whether their title and descrip-

tion indicated that the MHA (a) was developed for BPD,

(b) its description specifically refers to BPD (no general

skill training), (c) aims at those affected by BPD or their

relatives, and (d) met no other exclusion criteria (i.e., only

indicated in blended care settings, wearables or other

accessories necessary). The latter exclusion criteria were

applied to ensure comparability and detailed and com-

prehensive assessments of included MHAs. Duplicates

were excluded automatically and manually. Second, all

MHAs screened as eligible (both free and with costs)

were downloaded and assessed for further information.

The MHAs were downloaded and installed either on a

Huawei P10 lite (model WAS-LX1A) or an Apple iPhone

6 s (model NN0X2ZD/A). MHAs were included for the

rating and analysis if (a) they worked to the degree that

assessment was possible and (b) the provided content

addressed BPD interventionally (e.g., psychoeducation,

skills training).

Assessment ofincluded apps

Pairs of two independent reviewers with a degree in psy-

chology (KB, JW, JR) conducted the classification and

quality rating for all included apps. They were supervised

by a researcher with a Master’s degree in psychology and

in training to become a licensed psychotherapist (LSS),

who also solved classification and rating conflicts. In case

MHAs were available for different operating systems, the

MHAs were rated independently. The assessment was

performed using the German version (MARS-G; [21]) of

the Mobile Application Rating Scale (MARS; [22]).

General characteristics

An adapted version of the classification page of the

MARS-G was used to examine MHA characteristics. The

following information was assessed: (a) name, (b) plat-

form, (c) developer, (d) store link, (e) yearly price in Euro

(if available in other currencies, the price was converted),

(f) affiliation (i.e., unknown, commercial, university, non-

governmental organization, government), (g) user star

rating and the number of ratings (only user ratings with

a minimum number of three ratings were included), (h)

existence of a disclaimer in the MHA description that

the MHA does not replace psychotherapeutic treatment/

Page 3 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

diagnostics, (i) employed methods (i.e., psychoeduca-

tion, data collection, feedback, reminders, skills training,

mindfulness exercises, breathing exercises, relaxation

exercises, body exercises, goal tracking), and (j) privacy

and security details documentation (i.e., password pro-

tection, informed consent, contact details, login, emer-

gency functions, safety measurements for mobile loss).

Conflicts were defined as any disagreement.

Quality rating

The multidimensional MARS(−G) quality rating includes

19 items on four different main subscales, which are

evaluated on a five-point Likert scale (1 = inadequate,

2 = poor, 3 = acceptable, 4 = good, and 5 = excellent): (a)

engagement with five items (entertainment, interest, cus-

tomization, interactivity, target group), (b) functionality

with four items (performance, ease of use, navigation,

gestural design), (c) aesthetics with three items (layout,

graphics, visual appeal), and (d) information with seven

items (accuracy of MHA description, goals, quality of

information, the quantity of information, visual informa-

tion, credibility, evidence base; [21, 22]). The MARS has

excellent internal consistency (Cronbach’s alpha = .90)

and high levels of inter-rater reliability (IRR; two-way

mixed ICC= .79; 95% CI [.79, .83]; [22, 23]). Internal

consistencies of the four main subscales are also very

high (α = .80-89, Median = .85) and inter-rater reliabili-

ties are fair to excellent (ICC= .50-.80, Median = .65; [22,

23]). Correspondingly, the MARS-G shows acceptable to

excellent internal consistency for all subscales (ω = .74-

.91) as well as for the overall score (ω = .81, 95% CI [.74,

.86]; [21]). The correlations of the corresponding dimen-

sions of the MARS and MARS-G range from r= .93-.98

[21].

Furthermore, the potential therapeutic gain was

assessed as an additional subscale in accordance with the

MARS-G with four items (gain for patients, gain for ther-

apists, risks and side effects, ease of implementation into

routine healthcare; [21]).

The rating was performed by two independent and

trained reviewers.

Data analysis

Data analysis was conducted in R [24]. For all following

calculations, the ratings of the two raters were averaged.

Mean values and standard deviations were calculated for

each subscale and across all four main subscales (engage-

ment, functionality, aesthetics, information). In addition,

the correlations between the user rating and the mean

values of each subscale and the mean value across all

four main subscales were calculated. For quality assur-

ance, inter-rater reliability between the two raters was

examined by intraclass correlation based on a two-way

model with absolute agreement. Hereby, an ICC below

.50 is suggested to describe poor agreement, between .50

and .75 moderate agreement, between .75 and .90 good

agreement, and above .90 excellent agreement [25].

Results

The systematic search identified 2977 MHAs, of which

12 distinct MHAs with a total of 16 versions (0.5%) were

eligible for inclusion. Of these, 11 (68.8%) were devel-

oped for the Android operating system and five (31.3%)

for iOS. Three MHAs (Emoteo, DBT Coach, Simple

DBT Skills Diary Card) were available for both operating

systems. The process of inclusion is detailed in the flow

chart (Additional file2).

Annual prices of included MHAs ranged from 0 to

125.98€, 12 MHAs (75.0%) were free of initial costs.

However, four of the latter (33.3%) offered in-app pur-

chases. The majority of MHAs (n= 12; 75.0%) came from

a commercial source, followed by MHAs from universi-

ties (n= 2; 12.5%). One MHA each (6.3%) came from an

NGO and an unknown source. User ratings ranged from

3.0 to 5.0 stars (M= 4.1; SD= .62). The app store descrip-

tions of the majority of included MHAs (n= 9; 56.3%)

did not include a disclaimer about the non-replacement

of psychotherapeutic treatment/diagnostics even though

treatment guidelines do not suggest MHAs as a first-line

approach [26]. Detailed characteristics are displayed in

Additional file3.

With regard to employed methods, all included MHAs

contained psychoeducational content. Most MHAs

(n= 13; 81.3%) collected data (e.g., in terms of monitor-

ing, tracking). Moreover, eight MHAs (50.0%) contained

mindfulness exercises, six MHAs (37.5%) each contained

breathing exercises and feedback, five MHAs (31.3%)

skills training, four (25.0%) relaxation exercises, and

three (18.8%) reminders. None of the included MHAs

contained body exercises or goal tracking. A detailed list-

ing of employed methods can be found in Table1.

With regard to privacy and security details documenta-

tion, the majority of the included MHAs (n= 15; 93.8%)

provided contact details, and seven MHAs (43.8%)

offered password protection. Five MHAs (31.3%) con-

tained emergency functions, four (25.0%) included

informed consent, and three (18.8%) required a login.

Detailed privacy and security information for each

included MHA is displayed in Table2.

Four of the included MHAs (25.0%) were tested

empirically (“DBT Coach” and “Emoteo”, both available

for Android and iOS). Two uncontrolled pilot studies

on “DBT Coach” (n= 22; n= 16) showed decreased

emotion intensity, urges to self-harm and substance

use, depression, and distress related to the MHA and

Page 4 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

Table 1 Employed methods in included MHAs in descending order of the total quality mean score

Name Platform Background Psycho-

education Data collection Feedback Reminder Skills training Mindfulness

exercises Breathing

Exercises Relaxation

Exercises Body Exercises Goal Tracking

DBT Coach iOS DBT ✓ ✓ ✓ ✓ ✓

Skills2Go für Borderliner iOS DBT ✓ ✓ ✓ ✓ ✓

DBT Coach Android DBT ✓ ✓ ✓ ✓ ✓ ✓ ✓

DBT Travel Guide Android DBT ✓ ✓ ✓ ✓ ✓ ✓ ✓

Simple DBT Skills Diary

Card Android DBT ✓ ✓ ✓ ✓

Simple DBT Skills Diary

Card iOS DBT ✓ ✓ ✓ ✓ ✓

Borderline Personality

D Test iOS – ✓ ✓ ✓

Psychopedia Android – ✓ ✓ ✓

Borderline Explained

the truth about BPD Android – ✓

Emoteo iOS DBT ✓ ✓ ✓ ✓ ✓

Emoteo Android DBT ✓ ✓ ✓ ✓ ✓

Borderline‑Persönli‑

chkeitsstörung Android – ✓

Borderline Explained

Premium Android – ✓ ✓ ✓

PD Test ‑ Persönli‑

chkeitsstörungstest Android – ✓ ✓ ✓ ✓

Personality Disorder

Test Android – ✓ ✓

Borderline Personal‑

ity Disorder; Causes,

Treatment

Android – ✓

Page 5 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

good acceptability and usability when used as an

adjunct to treatment [27, 28]. One uncontrolled pilot

study on “Emoteo” as an adjunct to treatment with 16

participants showed high satisfaction with the MHA

and a significant decrease in aversive tension [29].

None randomized controlled clinical trial was identi-

fied for any of the included MHAs.

Quality rating

Overall, the quality of included MHAs over the four main

subscales was average (M= 3.25, SD= 0.68). Functional-

ity was the highest-rated subscale (M= 3.78, SD= 0.55),

followed by information (M= 3.35, SD= 0.69), aesthet-

ics (M= 3.00, SD= 0.83), and engagement (M= 2.87,

SD= 0.99). Results from the MARS-G quality rating are

displayed in Table3.

Table 2 Security and privacy details documentation of included MHAs in descending order of the total quality mean score

Name Platform Password

Protection Informed

Consent Contact Details Login Emergency

Functions Safety

Measurements for

Mobile Loss

DBT Coach iOS ✓ ✓ ✓ ✓

Skills2Go für Borderliner iOS ✓

DBT Coach Android ✓ ✓ ✓ ✓ ✓

DBT Travel Guide Android ✓ ✓ ✓

Simple DBT Skills Diary Card Android ✓ ✓ ✓

Simple DBT Skills Diary Card iOS ✓ ✓ ✓ ✓

Borderline Personality D Test iOS

Psychopedia Android ✓

Borderline Explained the truth about BPD Android ✓

Emoteo iOS ✓ ✓ ✓

Emoteo Android ✓ ✓ ✓

Borderline‑Persönlichkeitsstörung Android ✓

Borderline Explained Premium Android ✓

PD Test ‑ Persönlichkeitsstörungstest Android ✓ ✓

Personality Disorder Test Android ✓

Borderline Personality Disorder; Causes, Treatment Android ✓

Table 3 Means of the four main subscales of the MARS‑G (Messner et al., 2019) ratings in descending order of the total mean score

(range: 1 to 5)

Name Platform Engagement Functionality Aesthetics Information Overall Quality

DBT Coach iOS 4.4 4.8 5 4.1 4.6

Skills2Go für Borderliner iOS 4.2 4.8 4 4.3 4.3

DBT Coach Android 4.3 3.6 4.3 4 4.1

DBT Travel Guide Android 3.6 4.1 3.7 4.2 3.9

Simple DBT Skills Diary Card Android 3.9 3.9 3 3.8 3.7

Simple DBT Skills Diary Card iOS 3.7 3.5 2.5 3.7 3.3

Borderline Personality D Test iOS 2.3 4.2 2.8 3.3 3.2

Psychopedia Android 1.9 4 3 3.3 3.1

Borderline Explained the truth about BPD Android 2.2 4.1 2.7 3.1 3

Emoteo iOS 2.9 3 2.7 3.4 3

Borderline‑Persönlichkeitsstörung Android 2.1 3.9 2.8 2.7 2.9

Borderline Explained Premium Android 2.1 3.9 2.3 3.2 2.9

Emoteo Android 2.7 3.2 2.5 3.3 2.9

PD Test ‑ Persönlichkeitsstörungstest Android 1.9 3.1 2.2 2.8 2.5

Personality Disorder Test Android 2.2 3 2.2 2.5 2.5

Borderline Personality Disorder; Causes, Treatment Android 1.5 3.4 2.3 1.8 2.2

Page 6 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

Inter-rater reliability on item level across all four main

subscales was good (ICC= .90; 95% CI [.87, .92]) and reli-

abilities for each of the subscales separately ranged from

good to excellent (ICC= .75-.94).

No significant bivariate correlations were found

between the user ratings and the overall total score of

the MARS-G (r(11) = .26, 95% CI [−.34, .71]; p> .05) or

MARS-G main subscales (r(11) = .05-.32, p> .05).

The additional subscale on potential therapeutic gain

showed lower ratings (M= 2.67, SD= 0.83). Therapeu-

tic gain ratings per item across all apps are displayed in

Fig.1.

Discussion

The present study summarizes the findings of a system-

atic search and quality rating of MHAs for BPD in Ger-

man and British app stores (Apple App Store for iOS

MHAs and Google Play Store for Android MHAs). The

16 included MHAs showed varying quality with particu-

lar shortcomings in engagement, existing evidence base,

privacy and security details documentation, and poten-

tial therapeutic gain. This is in line with previous stud-

ies on MHAs for other mental health issues (e.g., [16,

30–34]). Of note, previous results on the validity of user

ratings (e.g., [14–16]) could also be reproduced, with no

correlations between these ratings and both the overall

quality of included MHAs and individual subscales.

Lack of engagement facilitating components seems to

be a recurring problem in MHAs for mental health dis-

orders (e.g., [16]), which points out the contradiction

between the unique possibilities MHAs offer in terms

of persuasive design (e.g., personalization, real-time

feedback [35, 36]; and the low adherence to MHAs in

real-world treatment [37]. In contrast, the subscale infor-

mation, arguably the most important subscale from a

therapeutic perspective, showed a moderate quality of

the included MHAs. However, the large range from 1.8

to 4.3 reflects the existence of both MHAs with almost all

relevant information provided in a concise and accurate

manner and MHAs that do not provide suitable informa-

tion at all.

While we were able to identify three studies on the

acceptability, usability, and effectiveness of four of the

included MHAs (25.0%), none of them was a randomized

controlled trial, highlighting the limited evidence base.

This may be due to the fast-moving nature of the field of

MHAs and the fact that they are scientifically developed

and evaluated. MHAs are often not made publicly avail-

able outside related studies [13]. However, it points also

toward the need for more research on actually available

Fig. 1 Ratings on the subscale therapeutic gain across all included MHAs. The item risk was renamed to security to clarify encoding directions.

Ratings lower than 2.5 were categorized as low, ratings between 2.5 and 4 as middle, and ratings of 4 and higher as high

Page 7 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

MHAs employing rigorous study designs to gain knowl-

edge on the effectiveness of available MHAs for BPD. Of

note, in Germany, there are efforts to establish a billing

model by which scientifically evaluated MHAs can be

prescribed by health care providers. However, the extent

of the scientific evaluation remains debated [38].

Whereas shortcomings in the area of privacy (e.g., in

terms of insufficient data protection, treatment with-

out informed consent) may be equally relevant for all

MHAs, security issues (e.g., lack of emergency functions)

may be particularly important in the treatment of BPD,

given patients’ self-harm tendencies, frequent crises, and

suicidal behaviors [3]. However, only five (31.2%) of the

included MHAs offered any such feature. Of note, this

builds a rather low threshold for privacy and data secu-

rity, given that the present study examined these domains

only on content and documentation level by means of the

MARS rating scale, not a technological level (e.g., threat

and vulnerability analysis, data handling). Hence, only

31% of all examined MHAs overcoming this basic thresh-

old can be viewed as alarming regarding patient safety in

the context of uncontrolled use of MHAs for BPD.

Observed shortcomings on the subscale of potential

therapeutic are also in line with concerns regarding the fit

of stand-alone MHAs for BPD patients. More precisely,

it is assumed that the need for interpersonal contact as

an important mechanism of treatment in this population

cannot be met in self-help IMIs [3]. Quite to the contrary,

interpersonal contact in face-to-face therapy is regarded

as an essential context and corrective experience for

dealing with conflicts and intensive emotions [3]. This

opinion of (face-to-face) interpersonal contact and thera-

peutic relationship being a necessary precondition for

psychotherapeutic approaches to work is not new to the

field of digital mental health care [39, 40]. However, evi-

dence of the last two decades has highlighted that thera-

peutic relationship can be established digitally, being

probably not as important as assumed, at least in a digital

treatment context and represent no necessary precondi-

tion for mental disorder treatments to work [39, 40]. Still,

this general statement needs to be examined and vali-

dated for each mental disorder separately and might be

particularly challenged in the context of personality dis-

orders. In this context, the recent systematic review and

meta-analysis on seven RCTs on two stand-alone MHAs

and three MHAs offered as an adjunct to other interven-

tions for adults with common BPD symptoms showed

no significant effect compared to control conditions (i.e.,

placebo, treatment-as-usual, waitlist, anger manage-

ment group that was available for both intervention and

control group) on symptoms (Hedges’ g= − 0.07, 95%

CI [−.25, .12]) and general psychopathology (Hedges’

g= 0.30, 95% CI [− 0.14, 0.75]; [13]). Thus, MHAs might

fall short, and the lack of interpersonal contact or thera-

peutic guidance [41] could be one reason for them being

not effective to date. Of note, though most included stud-

ies in this systematic review and meta-analysis did not

report any serious adverse events, many had strict inclu-

sion and exclusion criteria (e.g., regarding suicidal idea-

tion or severe depression). Assuming that effects in the

more complex BDP population not included in the stud-

ies are even smaller and adverse events may be more

common, this further limits the recommendation of pub-

licly available MHAs for BPD.

However, while the evidence on the effectiveness of

particularly stand-alone MHAs for BPD is scarce, well

developed and safe MHAs may still be beneficial if inte-

grated into a concept of blended psychotherapy, i.e., the

combination of online intervention elements with stand-

ard psychotherapeutic care [3, 42–46]. Blended therapy

may provide the possibility to combine both the need for

and safety of interpersonal contact in the treatment of

BPD and the advantages of IMIs and MHAs in particular

and offers several advantages beyond that (e.g., patient

empowerment, therapist support by standardized mate-

rials; [46–50]).

Limitations

When interpreting the results of this study, there are

some important limitations that must be considered.

First, the search results may be limited by the selected

search strings and the language restriction (English, Ger-

man, and French). While the present search strategy was

quite extensive in the number of different terms, none-

theless, future studies could expand the present findings

by including other languages. Second, MHAs indicated in

blended care settings or MHAs using wearables, or other

accessories were excluded, which might have further lim-

ited the number of included MHAs. Third, MHAs might

have been missed due to the restriction to the German

and British Google Play and Apple App Store due to the

limitations of the web crawler used. However, as Google

Play and Apple App Store together hold a market share

of around 99% [51], the loss of relevant MHAs should

be negligible. Yet, it cannot be guaranteed that the find-

ings of this study are transferable to the respective MHA

versions in other stores or that included MHAs are also

available in other major markets (e.g., United States,

India, China). Fourth, the availability of MHAs in stores

changes rapidly, and the present study must be under-

stood as a snapshot at the time of the search. Fifth, pri-

vacy and data security details documentation was only

assessed on a descriptive level. In line with the present

findings, a recent study on privacy and data security in

MHAs for depression and smoking cessation showed

inadequate or insufficient information in present privacy

Page 8 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

policies [52]. Therefore, a closer look at the privacy poli-

cies of MHAs may be worthwhile in future studies. Ulti-

mately, however, MHAs need to be tested regarding data

security and privacy by means of, e.g., thread analysis and

Medical Device Regulation testing [53–55]. Sixth, there

was only a limited number of MHAs included, which

decreases the power of correlation analyses. Conse-

quently, non-significant correlations between user ratings

and MHA quality should be interpreted with caution.

Implications

Despite these limitations, this study has several strengths,

including the extensive search strategy and the standard-

ized rating by two independent reviewers that allow an

overview of available MHAs for BPD and initial conclu-

sions. More precisely, regarding the usage of MHAs in

the treatment of BPD, results point to the need for further

studies on the efficacy, effectiveness and possible adverse

events of MHAs for BPD and independent information

platforms (e.g., Mobile Health App Database: http://

mhad. scien ce; MIND: https:// minda pps. org/) to provide

reliable information on the quality and content of MHAs.

Ultimately, only the combination of a thorough content,

system and evidence testing can inform about the quality

and thus recommendability of MHAs for BPD.

Conclusion

Our systematic rating of 16 MHAs for BPD indicated

an average overall quality following the standardized

MARS-G quality rating. Future MHAs should focus

more on the unique technical aspects of smartphones

to enhance engagement and pay more attention to pri-

vacy and security details. Evidence on the efficacy, effec-

tiveness and possible adverse events of MHAs for BPD

included in this systematic rating is limited, preventing

reliable recommendations regarding their usage. How-

ever, some of the available MHAs offer content that may

help to close the treatment gap, at least in the blended

therapy context.

Abbreviations

BPD: Borderline Personality Disorder; DBT: Dialectical behavior therapy; IMI:

Internet‑ and mobile‑based intervention; MHA: Mobile health application;

MARS: Mobile Application Rating Scale (Stoyanov et al., 2015); MARS‑G:

Mobile Application Rating Scale – German version (Messner et al., 2020);

MBT: Mentalization‑based therapy; MHAD: Mobile Health App Database; RCT :

Randomized controlled trial.

Supplementary Information

The online version contains supplementary material available at https:// doi.

org/ 10. 1186/ s40479‑ 022‑ 00186‑w.

Additional file1.

Additional file2.

Additional file3.

Acknowledgements

The authors would like to thank Rüdiger Pryss, Robin Kraft, Pascal Damasch,

and Philipp Dörzenbach for their support in the MHAD project. They would

also like to thank Julia Weresch (JW), Charlotte Dechmann, Chiara Ritter,

Isabelle Keller, Bettina Salger, and Katja Barck (KB) for supporting the data

collection.

Authors’ contributions

EMM, YT, LBS, and HB have developed the study design. LSS and LS initiated

this study. MS helped compile the app data. LSS, JR, and LBS have collected

the data. LS, LSS, JR, and YT run the statistical evaluations. LSS wrote the first

draft of the article. All authors contributed to the current version of the article

and have approved the final paper.

Funding

Open Access funding enabled and organized by Projekt DEAL. None reported.

Availability of data and materials

The datasets used and analyzed during the current study are available from

the corresponding author on reasonable request. Data will only be shared for

scientific purposes. Data sharing agreements may have to be signed depend‑

ing on the request. Support is depending on current resources.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

EMM, YT, MS, LBS, and HB, developed and run the German Mobile Health App

Database (MHAD) project. MHAD is a self‑funded project at Ulm University

with no commercial interests. HB, LBS and EMM received payments for talks

and workshops in the context of e‑mental‑health. HB is (principal) investiga‑

tor of several third‑party funded projects on IMIs. All other authors declare no

competing interests.

Author details

1 Institute of Psychology and Education, Department of Clinical Psychology

and Psychotherapy, Ulm University, Ulm, Germany. 2 Institute of Databases

and Information Systems (DBIS), Ulm University, Ulm, Germany. 3 Institute

of Psychology, Department of Rehabilitation Psychology and Psychotherapy,

Albert‑Ludwigs‑University of Freiburg, Freiburg, Germany.

Received: 20 October 2021 Accepted: 8 May 2022

References

1. American Psychiatric Association. DSM‑5: diagnostic and statistical

manual of mental disorders 5th edition. Washington, DC, US: American

Psychiatric Association; 2013.

2. Stoffers‑Winterling JM, Völlm BA, Rücker G, Timmer A, Huband N, Lieb K.

Psychological therapies for people with borderline personality disorder.

Cochrane Database Syst Rev. 2012;8.

3. Fassbinder E, Hauer A, Schaich A, Schweiger U, Jacob GA, Arntz A. Integra‑

tion of e‑health tools into face‑to‑face psychotherapy for borderline

personality disorder: a chance to close the gap between demand and

supply? J Clin Psychol. 2015;71(8):764–77.

4. Hermens MLM, Van Splunteren PT, Van Den Bosch A, Verheul R. Barriers to

implementing the clinical guideline on borderline personality disorder in

the Netherlands. Psychiatr Serv. 2011;62(11):1381–3.

Page 9 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

5. Jobst A, Hörz S, Birkhofer A, Martius P, Rentrop M. Einstellung von Psycho‑

therapeuten gegenüber der Behandlung von Patienten mit Borderline

Persönlichkeitsstörung. PPmP Psychother Psychosom Medizinische

Psychol. 2010;60(3–4):126–31.

6. Andersson G, Carlbring P, Titov N, Lindefors N. Internet interventions for

adults with anxiety and mood disorders: a narrative umbrella review of

recent Meta‑analyses. Can J Psychiatr. 2019;64(7):465–70.

7. Andersson G, Titov N. Advantages and limitations of internet‑based

interventions for common mental disorders. World Psychiatry.

2014;13(1):4–11.

8. Carlbring P, Andersson G, Cuijpers P, Riper H, Hedman‑Lagerlöf E.

Internet‑based vs. face‑to‑face cognitive behavior therapy for psychiatric

and somatic disorders: an updated systematic review and meta‑analysis.

Cogn Behav Ther. 2018;47(1):1–18.

9. Domhardt M, Geßlein H, von Rezori RE, Baumeister H. Internet‑ and

mobile‑based interventions for anxiety disorders: a meta‑analytic review

of intervention components. Depress Anxiety. 2019;36(3):213–24.

10. Steubl L, Sachser C, Baumeister H, Domhardt M. Mechanisms of change

in internet‑ and mobile‑based interventions for PTSD: a systematic review

and meta‑analysis. Eur J Psychotraumatol. 2021;12(1):1879551.

11. Weisel KK, Fuhrmann LM, Berking M, Baumeister H, Cuijpers P, Ebert DD.

Stand‑alone smartphone apps for mental health—a systematic review

and meta‑analysis npj. Digit Med. 2019;2(1):1–10.

12. Frías Á, Solves L, Navarro S, Palma C, Farriols N, Aliaga F, et al. Technology‑

based psychosocial interventions for people with borderline person‑

ality disorder: a scoping review of the literature. Psychopathology.

2020;53(5–6):254–63.

13. Ilagan GS, Iliakis EA, Wilks CR, Vahia IV, Vahia IV, Choi‑Kain LW, et al. Smart‑

phone applications targeting borderline personality disorder symptoms:

a systematic review and meta‑analysis. Borderline Personal Disord Emot

Dysregulation. 2020;7(1):1–15.

14. Nicholas J, Fogarty AS, Boydell K, Christensen H. The reviews are in: A

qualitative content analysis of consumer perspectives on apps for bipolar

disorder [Internet]. Vol. 19, Journal of Medical Internet Research. JMIR

Publications Inc.; 2017. p. e7273.

15. Singh K, Drouin K, Newmark LP, Jae HL, Faxvaag A, Rozenblum R, et al.

Many mobile health apps target high‑need, high‑cost populations, but

gaps remain. Health Aff. 2016;35(12):2310–8.

16. Terhorst Y, Messner EM, Schultchen D, Paganini S, Portenhauser A, Eder

AS, et al. Systematic evaluation of content and quality of English and Ger‑

man pain apps in European app stores. Internet Interv. 2021;24:100376.

17. Huckvale K, Nicholas J, Torous J, Larsen ME. Smartphone apps for the

treatment of mental health conditions: status and considerations. Vol. 36,

Current Opinion in Psychology. Elsevier BV; 2020. p. 65–70.

18. Stach M, Kraft R, Probst T, Messner EM, Terhorst Y, Baumeister H, et al.

Mobile health app database ‑ a repository for quality ratings of mhealth

apps: Proceedings ‑ IEEE Symposium on Computer‑Based Medical

Systems. Institute of Electrical and Electronics Engineers Inc.; 2020. p.

427–32.

19. Portenhauser AA, Terhorst Y, Schultchen D, Sander LB, Denkinger MD,

Stach M, et al. Mobile apps for older adults: systematic search and evalua‑

tion within online stores. JMIR Aging. 2021;4(1):e23313.

20. Schultchen D, Terhorst Y, Holderied T, Stach M, Messner EM, Baumeister H,

et al. Stay present with your phone: a systematic review and standardized

rating of mindfulness apps in European app stores. Int J Behav Med. 2020.

21. Messner EM, Terhorst Y, Barke A, Baumeister H, Stoyanov S, Hides L, et al.

The german version of the mobile app rating scale (MARS‑G): develop‑

ment and validation study. JMIR mHealth uHealth. 2020;8(3):1–9.

22. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani

M. Mobile app rating scale: A new tool for assessing the quality of health

mobile apps. JMIR mHealth uHealth. 2015;3:1.

23. Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M,

et al. Validation of the Mobile application rating scale (MARS). PLoS One.

2020;15(11):e0241480.

24. Core R. Team. R: a language and environment for statistical computing

[internet]. Vienna, Austria: R Foundation for statistical. Computing. 2020;

Available from: https:// www.r‑ proje ct. org/.

25. Koo TK, Li MY. A guideline of selecting and reporting Intraclass cor‑

relation coefficients for reliability research. J Chiropr Med. 2016 Jun

1;15(2):155–63.

26. National Institute for Health and Clinical Excellence. Borderline personal‑

ity disorder ‑ the NICE guideline on treatment and management. London:

The Brititish Psychological Society & The Royal College of Psychiatrists;

2018.

27. Rizvi SL, Hughes CD, Thomas MC. The DBT coach mobile application as

an adjunct to treatment for suicidal and self‑injuring individuals with

borderline personality disorder: a preliminary evaluation and challenges

to client utilization. Psychol Serv. 2016;13(4):380–8.

28. Rizvi SL, Dimeff LA, Skutch J, Carroll D, Linehan MM. A pilot study of the

DBT coach: an interactive Mobile phone application for individuals with

borderline personality disorder and substance use disorder. Behav Ther.

2011;42(4):589–600.

29. Prada P, Zamberg I, Bouillault G, Jimenez N, Zimmermann J, Hasler R, et al.

EMOTEO: a smartphone application for monitoring and reducing aversive

tension in borderline personality disorder patients, a pilot study. Perspect

Psychiatr Care. 2017;53(4):289–98.

30. Kertz SJ, Maclaren Kelly J, Stevens KT, Schrock M, Danitz SB. A Review of

Free iPhone Applications Designed to Target Anxiety and Worry. 2017.

31. Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar

disorder: a systematic review of features and content quality. J Med

Internet Res. 2015;17(8):e4581.

32. Sander LB, Schorndanner J, Terhorst Y, Spanhel K, Pryss R, Baumeister

H, et al. “Help for trauma from the app stores?” a systematic review and

standardised rating of apps for Post‑Traumatic Stress Disorder (PTSD). Eur

J Psychotraumatol. 2020;11(1).

33. Sucala M, Cuijpers P, Muench F, Cardoș R, Soflau R, Dobrean A, et al. Anxi‑

ety: there is an app for that. A systematic review of anxiety apps. Depress

Anxiety. 2017;34(6):518–25.

34. Terhorst Y, Rathner E‑M, Baumeister H, Sander L. «Hilfe aus dem App‑

Store?»: Eine systematische Übersichtsarbeit und Evaluation von Apps zur

Anwendung bei Depressionen. Verhaltenstherapie. 2018;28(2):101–12.

35. Baumeister H, Kraft R, Baumel A, Pryss R, Messner E‑M. Persuasive E‑health

Design for Behavior Change. In: Baumeister H, Montag C, editors. Digital

Phenotyping and Mobile sensing: new developments in Psychoinformat‑

ics. Cham: Springer; 2019. p. 261–76.

36. Idrees AR, Kraft R, Pryss R, Reichert M, Baumeister H. Literature‑based

requirements analysis review of persuasive systems design for mental

health applications. In: Procedia Computer Science. Elsevier BV; 2021. p.

143–50.

37. 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;21(9):e14567.

38. Gkv‑Spitzenverband. Bericht des GKV‑Spitzenverbandes über die

Inanspruchnahme und Entwicklung der Versorgung mit Digitalen

Gesundheitsanwendungen. Berlin; 2021. Available from: https:// www.

gkv‑ spitz enver band. de/ media/ dokum ente/ krank enver siche rung_1/

telem atik/ digit ales/ 2021_ DiGA‑ Beric ht_ 33_ Abs6_ barri erefr ei. pdf

39. Berger T. The therapeutic alliance in internet interventions: a nar‑

rative review and suggestions for future research. Psychother Res.

2017;27(5):511–24.

40. Sucala M, Schnur JB, Constantino MJ, Miller SJ, Brackman EH, Guy ;, et al.

The Therapeutic Relationship in E‑Therapy for Mental Health: A System‑

atic Review. J Med Internet Res. 2012;14(4):e110.

41. Baumeister H, Reichler L, Munzinger M, Lin J. The impact of guidance

on internet‑based mental health interventions — a systematic review.

Internet Interv. 2014;1(4):205–15.

42. Baumeister H, Grässle C, Ebert DD, Krämer LV. Blended Psychotherapy –

verzahnte Psychotherapie: Das Beste aus zwei Welten? PiD ‑ Psychother

im Dialog. 2018;19(4):33–8.

43. Baumeister H, Bauereiss N, Zarski A‑C, Braun L, Buntrock C, Hoherz C, et al.

Clinical and cost‑effectiveness of PSYCHOnlineTHERAPY: study protocol

of a multicenter blended outpatient psychotherapy cluster randomized

controlled trial for patients with depressive and anxiety disorders. Front

Psychiatry. 2021;12:656.

44. Erbe D, Psych D, Eichert HC, Riper H, Ebert DD. Blending face‑to‑face and

internet‑based interventions for the treatment of mental disorders in

adults: systematic review. J Med Internet Res. 2017;19(9):1–14.

45. Kemmeren LL, van Schaik DJF, Riper H, Kleiboer AM, Bosmans JE, Smit JH.

Effectiveness of blended depression treatment for adults in specialized

mental healthcare: study protocol for a randomized controlled trial. BMC

Psychiatry. 2016;16(1):1–14.

Page 10 of 10

Steubletal. Borderline Personality Disorder and Emotion Dysregulation (2022) 9:17

•

fast, convenient online submission

•

thorough peer review by experienced researchers in your field

•

rapid publication on acceptance

•

support for research data, including large and complex data types

•

gold Open Access which fosters wider collaboration and increased citations

maximum visibility for your research: over 100M website views per year

•

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

Ready to submit your research

? Choose BMC and benefit from:

46. Kleiboer A, Smit J, Bosmans J, Ruwaard J, Andersson G, Topooco N, et al.

European COMPARative effectiveness research on blended depression

treatment versus treatment‑as‑usual (E‑COMPARED): study protocol for a

randomized controlled, non‑inferiority trial in eight European countries.

Trials. 2016;17(1):387.

47. Baumeister H, Terhorst Y, Grässle C, Freudenstein M, Nübling R, Ebert

DD. Impact of an acceptance facilitating intervention on psychothera‑

pists’ acceptance of blended therapy. Doering S, editor. PLoS One.

2020;15(8):e0236995.

48. Renn BN, Hoeft TJ, Lee HS, Bauer AM, Areán PA. Preference for in‑person

psychotherapy versus digital psychotherapy options for depression:

survey of adults in the US npj. Digit Med. 2019;2(1):1–7.

49. Schuster R, Pokorny R, Berger T, Topooco N, Laireiter AR. The advantages

and disadvantages of online and blended therapy: Survey study amongst

licensed psychotherapists in Austria. J Med Internet Res. 2018:e11007.

50. Titzler I, Saruhanjan K, Kerking M, Riper H, Ebert DD. Barriers and facilita‑

tors for the implementation of blended psychotherapy for depres‑

sion: a qualitative pilot study of therapists’ perspective. Internet Interv.

2018;12:150–64.

51. Statcounter. Mobile Operating System Market Share Worldwide. 2021.

Available from: https:// gs. statc ounter. com/ os‑ market‑ share/ mobile/

world wide

52. Huckvale K, Torous J, Larsen ME. Assessment of the data sharing and

privacy practices of smartphone apps for depression and smoking cessa‑

tion. JAMA Netw Open. 2019;2(4):e192542.

53. Knorr K, Aspinall D. Security testing for Android mHealth apps. In: 2015

IEEE 8th International Conference on Software Testing, Verification and

Validation Workshops, ICSTW 2015 ‑ Proceedings. Institute of Electrical

and Electronics Engineers Inc.; 2015.

54. Holfelder M, Mulansky L, Schlee W, Baumeister H, Schobel J, Greger H,

et al. Medical device regulation efforts for mHealth apps ‑ an experience

report of Corona check and Corona health. 2021.

55. Benjumea J, Ropero J, Rivera‑Romero O, Dorronzoro‑Zubiete E, Carrasco

A. Privacy Assessment in Mobile Health Apps: Scoping Review. JMIR

mHealth uHealth. 2020:e18868.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in pub‑

lished maps and institutional affiliations.