Mobile Applications for Self-management of Chronic Diseases: A

Systematic Review

Benjamin Stahr

, Sebastian Fudickar

2 a

and Christian Lins

3 b

Carl von Ossietzky University Oldenburg, Germany

Institute of Medical Informatics, University of L

ubeck, Germany

Department of Computer Science, Faculty of Engineering and Computer Science,

Hamburg University of Applied Sciences, Germany

Keywords:

Chronic Disease, mHealth, Self-management, Quantiﬁed Self, Health Apps.

Abstract:

Objectives: Since apps have been gaining popularity, they are also used to support the treatment of chronic

diseases. However, the effectiveness of these measures has not been fully conﬁrmed. This review deals with

features that make these apps effective. Methods: In this structured literature survey, relevant studies from the

year 2014 to 2019 were identiﬁed. Inclusion criteria were that the study included an app that was used to alle-

viate symptoms of chronic diseases or was intended to support the preventive treatment of patients. Results:

Ten studies were examined in detail, of which seven found signiﬁcant effects. Factors, which increase the ef-

fectiveness of mHealth apps include easy integration into everyday life, appropriate training of users, tailoring

the app to the target group, focusing on improving the relationship between user and disease, and user-speciﬁc

treatment of symptoms. Tracking of symptoms, education, and a chat can also increase effectiveness. Conclu-

sions: Most of the papers reviewed showed a positive impact of mobile apps on chronic disease progression.

However, a negative factor was also identiﬁed, in which patients became more involved with their illnesses as

a result of the intervention, which increased the perceived severity of the illness and thus reduced the quality

of life.

1 INTRODUCTION

Chronic diseases, such as type 2 diabetes (Chatter-

jee et al., 2017) or autoimmune thyroiditis (Antonelli

et al., 2015), are a major challenge to our modern

society. For example, the percentage of people af-

fected with diabetes worldwide has almost doubled

since 1980 from 4.7% to 8.5% in 2014 (Roglic, 2016).

Cardiovascular diseases are also widespread, account-

ing for 31% of all deaths worldwide (Finger et al.,

2016). In response to these new challenges to the

health care system, innovative and cost-effective mea-

sures are essential. One possibility are mobile inter-

ventions via mobile apps. In the mHealth sector there

are already many measures in place to improve care

and save costs in the health care system (Williams,

2012). However, the effectiveness of mHealth apps

as an intervention for chronic diseases is currently

controversial. Therefore, this systematic review aims

https://orcid.org/0000-0002-3553-5131

https://orcid.org/0000-0003-3714-0069

to investigate whether apps as a measure for therapy

support have a positive (or negative) inﬂuence on the

course of chronic diseases. In addition, it will be in-

vestigated which characteristics distinguish apps that

have been shown to have a positive inﬂuence on the

course of the disease from others, in order to identify

possible positive factors inﬂuencing these apps.

2 METHODS

Studies were analyzed with regard to the participants,

the intervention, length, study design, functionality of

the app used, and the results produced. This was done

with the aim of evaluating the inﬂuence of mHealth

interventions and, where appropriate, to record gen-

eral positive factors inﬂuencing the course of a dis-

ease.

542

Stahr, B., Fudickar, S. and Lins, C.

Mobile Applications for Self-management of Chronic Diseases: A Systematic Review.

DOI: 10.5220/0010846200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 542-548

ISBN: 978-989-758-552-4; ISSN: 2184-4305

2.1 Search Strategy

A search was conducted using the ORBISplus meta-

search engine

of the University of Oldenburg. The

number of results of the following sources are writ-

ten in braces after the respective source. Duplicates

were automatically eliminated by the meta search en-

gine and results from the following databases were in-

cluded: Medline (194), Pubmed Central (111), Gale

Academic OneFile (108), Health Reference Center-

Academic of Gale (91), Directory of Open Access

Journals (47), Springer Cross-Ref (40), Springer Link

(34), SAGE Publications CrossRef (22), SAGE Pub-

lications (21), Oxford Academic (17), Oxford Uni-

versity Press CrossRef (16), Public Library of Sci-

ence Cross-Ref (12), Elsevier CrossRef (12), Springer

Link Open Access (7), BMJ Journals (4), Digital

Commons by bpress (2), Association for Computing

Machinery CrossRef (2), Mary Ann Liebert CrossRef

(2), Wolters Kluwer Olvid CrossRef (2), and Amer-

ican Chemical Society CrossRef (1). Only scien-

tiﬁc articles written in English and published between

2014 and 2019 were accepted. The search term used

was: ”chronic disease” AND ”self-management”

AND ”app” AND ”smartphone” AND (”mHealth”

OR ”mobile health”).

2.2 Selection of Studies

The following inclusion criteria were used to select

the studies: the study included an app; the app was

used to alleviate symptoms or for the preventive treat-

ment of a chronic disease; the effect of the app was

tested in human trials involving patients. Studies were

excluded: review articles; additional use of devices

developed for the study; were not completed at the

time of the search; there were several applications in

the study, e.g. doctors also have an app as well as

patients.

2.3 Data Extraction

The meta-data of the selected articles were entered

into a spreadsheet, divided into the following items:

Author, year, purpose of the study, nature of the sam-

ple of participating subjects, type of disease, measure-

ments performed, results of the study, length of the

intervention, name of the app, nature of the interven-

tion, and training of the subjects to use the app.

See https://plus.orbis-oldenburg.de/

IdentiﬁcationScreening

Detail

Screening

Result

Paper found in

database (n = 232)

Title and abstract

screened (n = 232)

Paper excluded

(n = 209)

Fulltexts screened

(n = 23)

Fulltexts reviewed

(n = 10)

Paper excluded

(n = 13)

Reasons:

● No standalone app: 3

● Eect on user not tested: 2

● Disease unclear: 4

● Unknown subject group: 1

● App targets doctors: 2

● Unusable objective of the study: 1

Figure 1: Article selection in style of PRISMA (Moher

et al., 2009).

3 RESULTS

The ﬂow chart in Figure 1 shows the selection pro-

cess for the studies to be included. Out of 232 results

of the search, ten studies met the underlying criteria.

The studies found are concerned with various chronic

diseases. The included apps addressed diabetes type

2, various cardiovascular diseases, obstructive sleep

apnea, asymptomatic osteoporosis, gout and HIV. Of

the ten selected trials, six were randomized controlled

trials, while four trials lacked a control group. Seven

of the ten studies could demonstrate signiﬁcant effects

related to the app, but there was only one study that

found an improvement in the symptoms of a chronic

disease due to the app used. Three studies could

not show signiﬁcant effects, e.g. caused by collect-

ing mostly qualitative data via semi-structured inter-

views. In another, the focus of the research was not

on demonstrating signiﬁcant effects. Tables 1 provide

an overview of the evaluated studies and Tables 2 pro-

vide a detailed insight into the respective intervention

of each study.

3.1 Studies with Signiﬁcant Effects

The following studies were able to demonstrate a

number of different signiﬁcant effects. These in-

cluded improved self-management, symptom relief,

better understanding of disease, changes in eating

habits, greater adherence to medical interventions and

more frequent visits to the doctor. The following re-

sults are all signiﬁcant with p < 0.05. In a Norwe-

Mobile Applications for Self-management of Chronic Diseases: A Systematic Review

543

gian study, Holmen et al. (Holmen et al., 2014) have

shown that mHealth intervention can improve self-

management and understanding of the disease in peo-

ple with diabetes. In a New Zealand study, Eyles et

al. (Eyles et al., 2017) showed that an app could sig-

niﬁcantly reduce the salt consumption of people with

cardiovascular disease. Serlachius et al. (Serlachius

et al., 2019) have shown in another New Zealand

study that an app can increase the understanding of

gout disease in people with gout, but participants per-

ceived their disease and symptoms more negatively

after the intervention, and in general their attitude to

gout became more negative. In a study on mobile

interventions from the USA, Schnall et al. (Schnall

et al., 2018) were able to demonstrate a clear improve-

ment in ﬁve of 13 symptoms of AIDS. In Singapore,

a study by Hui Zhang et al. (Zhang et al., 2017) in-

vestigated the possibility of clarifying cardiovascular

diseases using an app. It was found that the study

participants had a better understanding of coronary

heart disease and its consequences after the interven-

tion. In addition, participants controlled their choles-

terol levels more frequently as a result of the study.

In Spain, Isetta et al. (Isetta et al., 2017) showed that

an app was able to improve the adherence of patients

with obstructive sleep apnea to CPAP ventilation. In

the USA, Dillingham el al. (Dillingham et al., 2018)

showed that the consistency of patient visits to the

doctor during the phase of mHealth intervention has

increased, but this was not related to the frequency of

use of the app.

3.2 Studies without Signiﬁcant Effects

In the following three studies no signiﬁcant results

could be shown for various reasons, but it is important

to note that two of the three studies were more quali-

tative research and therefore no signiﬁcant results can

be deduced from them. In a Canadian study by Agar-

wal et al. (Agarwal et al., 2019) on the improvement

of symptoms of diabetes, for example, no positive ef-

fects could be proven, according to the authors this

could be due to the fact that the app was handed out

to participants on an additional smartphone, with all

other features of the smartphone disabled, which in-

creases the inhibition for using the app. In a Danish

study by Ravn Jakobsen et al. (Ravn Jakobsen et al.,

2018) the qualitative approach was the reason why

there was no signiﬁcant result. Semi-structured inter-

views were conducted to collect information, which

hardly allow a statistical evaluation, but provide infor-

mation on possible positive inﬂuencing factors. Fur-

thermore, the participants were very satisﬁed with the

intervention, which indicates the quality of the inter-

vention and the app. The US study by Brewer et al.

(Brewer et al., 2019), which mainly dealt with the de-

velopment cycle of mHealth interventions, was simi-

larly qualitative. The inﬂuence of the developed app

was tested with a survey of participant satisfaction,

which was very high.

3.3 Summary of Interventions

The various interventions were characterized by the

fact that they were all built around speciﬁc apps,

which held similar features. Common features in-

cluded reminders for doctor visits and tracking of

symptoms, physical activity, mental health, personal

goals or eating habits. To use the tracked information,

users were often sent a therapy recommendation to

reduce their symptoms or motivating messages from

the app. In addition, information about the disease to

be treated was often included in the apps to educate

the users in order to give the participants a broader

knowledge of the respective disease. In order to con-

solidate the knowledge acquired, playful approaches

were implemented in some cases, for example in the

form of various quizzes in the case of Dillingham et

al (Dillingham et al., 2018). Frequently, a link to sup-

port was also directly integrated into the app to help

participants with problems. The feature of connecting

all users of the app via a chat to exchange information

and increase motivation also appeared; this feature

was rated very positively by the users. In the study

by Ravn Jakobsen et al (Ravn Jakobsen et al., 2018),

users were also able to view the results of their bone

scans. In the study by Eyles et al. (Eyles et al., 2017),

by scanning barcodes on food, users were able to de-

termine the salt content of the food and view a pur-

chase recommendation for the product. Apart from

the apps, interventions occasionally included coun-

seling services or reminders by text messages. The

length of the interventions varied between two weeks

and one year with an average length of 13.4 weeks. In

six of the studies, participants received help in using

the app, for example by technical support answering

questions, download assistance or training by phone.

A tendency can be seen that studies which did not

have a support offer sometimes lost more users. This

could be due to the fact that the average study partic-

ipant may lack sufﬁcient technical understanding to

use the app.

4 DISCUSSION

The results of the evaluated studies have shown that

mHealth interventions often lead to signiﬁcant effects

HEALTHINF 2022 - 15th International Conference on Health Informatics

544

on the participants. These can improve the patient’s

situation, as found by Schnall et al (Schnall et al.,

2018), or have a positive effect on other factors, such

as self-management. On the other hand, there are

studies that found no or only few positive develop-

ments in the participants. In the following, we discuss

which factors may have had a positive inﬂuence on

participants in the studies and, accordingly, may have

led to a reduction in possible positive effects of the

mHealth interventions. Afterwards, factors are exam-

ined that could have had a positive effect on different

mHealth interventions.

4.1 Positive Factors for Intervention

As various studies have shown positive effects, fea-

tures can be identiﬁed in these studies which may

have had a positive effect on the progression of the

disease or other areas. The fact that the study by

Schnall et al. (Schnall et al., 2018) showed signiﬁcant

improvements in ﬁve out of 13 symptoms of AIDS

may be due to the fact that the app always suggested

a measure appropriate to the symptoms of the partici-

pants. This speciﬁc procedure for each individual pa-

tient can be a reason for the success of the mHealth

intervention. In the study by Ravn Jakobsen et al.

(Ravn Jakobsen et al., 2018), participants were able

to view the results of their bone scans without having

to travel to the hospital, which was considered very

useful by the participants. It can be concluded from

this that advantages that are exclusively attributable

to the app can lead to an increase in use. General

education, possibly in the context of measures to in-

crease knowledge, can also improve understanding

of a disease and thus provide a basis for further ac-

tion, as shown by Zhang et al (Zhang et al., 2017).

Tracking of symptoms can also have a positive inﬂu-

ence on patients, as the study by Holmen et al. (Hol-

men et al., 2014) has shown a signiﬁcantly better self-

management among participants who used an app for

the exclusive tracking of symptoms. In this context,

improved self-management can again be seen as the

basis for further measures to improve symptoms. In

the study by Brewer et al. (Brewer et al., 2019), users

reported a very high level of satisfaction with the app,

averaging nine out of ten points. In this study, this can

be attributed to the fact that the users were strongly

involved in the development process of the app, and

that there was an opportunity to exchange ideas with

other participants in the app, which was also consid-

ered very useful, since one saw oneself as part of a

community. From this it can be concluded that when

developing an app in the ﬁeld of mHealth, one should

always make sure that it ﬁts the chosen target group.

In addition, contact with other affected people seems

to be helpful, as this creates a sense of community

among those affected, which can lead to higher moti-

vation to improve one’s lifestyle.

4.2 Negative Factors for Intervention

Various factors may have negative inﬂuence on the

course of the chronic disease or on the success of the

app. Among Agarwal et al. (Agarwal et al., 2019),

one important factor was that an additional smart-

phone was distributed to participants that had all other

features disabled. This additional barrier to use may

have meant that no signiﬁcant results were found in

the study. Thus, an app should be as easy as possible

to integrate into the daily life of the patients in order to

maximize the usage. Another factor is help in adapt-

ing the technology. In the studies examined, the trend

is evident that a lack of technical support has reduced

the use of the app, since for some participants the gen-

eral handling of apps is a hurdle. Some participants

in the study by Ravn Jakobsen et al. (Ravn Jakob-

sen et al., 2018) had problems downloading the app.

Therefore, the difﬁculty of using an app should al-

ways match the technical understanding of its users

or training should be provided for users in order to at-

tract as many users as possible to the app. It is also

important to note that in the study by Serlachius et al.

(Serlachius et al., 2019) the participants’ understand-

ing of the disease improved, but on the other hand the

participants perceived their disease more negatively

and interpreted their symptoms as more serious. Here

it is positive that participants were able to develop a

better understanding of their disease, but the app fails

to have the desired effect if it leads to a higher psycho-

logical impact. The result is that it leads to a greater

burden on the users instead of helping them. There-

fore, in an app more emphasis should be placed on im-

proving the relationship between the patient and the

disease, instead of just treating the patient in relation

to the to her or his illness.

5 CONCLUSION

It has been shown that mHealth interventions via

smartphone apps often have a positive inﬂuence on

test persons. These consist less often in an improve-

ment of the symptoms, but more often in an improve-

ment of the understanding of the disease or improve-

ment of the self-management of the test persons. In

addition, mHealth interventions and the apps used

should meet certain criteria to increase their effective-

ness. For example, the app should be easily integrated

Mobile Applications for Self-management of Chronic Diseases: A Systematic Review

545

into the users’ everyday life and the difﬁculty of its

use should be adapted to the users or sufﬁcient train-

ing should be provided. Also, the app should focus

on improving the relationship between user and dis-

ease, rather than focusing solely on the disease. The

feedback of an app should be speciﬁcally tailored to

the symptoms of each individual user. In addition,

general information about an illness and tracking of

symptoms and behavior are helpful. Exclusive fea-

tures, such as the quick provision of bone scans, can

be a further incentive to use. By involving poten-

tial users in the development, features can be adapted

to target groups, thus increasing trust in the app. A

chat, which connects all users of the app with each

other, can additionally increase the motivation and

frequency of using an app, as it promotes a sense of

community between the users. These possible posi-

tive inﬂuencing factors need further research, but they

offer a fundamental guideline for the development of

future mHealth apps.

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Table 1: Overview of the evaluated studies.

# Year, Authors Study purpose (Disease) Characteristics of the sample Length App name

1 2014, Holmen et al.

(Holmen et al., 2014)

Evaluate the usefulness of an app

for logging symptoms and medical

advice in terms of improving symp-

toms of type 2 diabetes. (Type 2 di-

abetes)

General: (n = 151); randomized with control

group; inclusion if age ≥ 18 years; understand

Norwegian; able to use systems to improve self-

management; HbA1c level > 7.0%;

1 year Few Touch

Application

2 2019, Agarwal et

al. (Agarwal et al.,

2019)

Evaluate whether the BlueStar app

leads to improved HbA1c levels and

improves patient self-management.

(Type 2 diabetes)

General: (n = 110); randomized with control

group; recruited from three Ontario diabetes ed-

ucation programs; inclusion if HbA1c ≥ 8.0% in

the past three months; Age ≥ 18 years; patients

were part of a diabetes education program; partic-

ipants have an E-Mail address; able to understand

and write English; exclusion if diagnosed with

type 1 diabetes; continuous monitoring of blood

glucose levels; possession of an insulin pump; un-

able to use a computer;

6 months BlueStar

3 2018, Schnall et al.

(Schnall et al., 2018)

Evaluating the usefulness of the app

mVIP in alleviating HIV-related

symptoms. (AIDS)

General: (n = 80); randomized with control

group; inclusion if age ≥ 18 years; English-

speaking; diagnosed with HIV; onset of at least

two HIV-related symptoms in the past week;

Mini-Mental State Examination (MMSE) com-

pleted with at least 24 points; in possession of a

smartphone or tablet;

12 weeks mVIP

4 2017, Zhang et al.

(Zhang et al., 2017)

Evaluate the effectiveness of an app

in improving awareness and knowl-

edge of coronary artery disease, as

well as reducing stress and improv-

ing heart-related lifestyles. (Coro-

nary heart disease)

General: (n = 80); randomized with control

group; inclusion if age ≥ 21 years and age ≤ 65;

in possession of a smartphone; English-speaking;

working full-time; exclusion if diagnosed with

heart disease; working in the medical sector; par-

ticipating in other program related to heart dis-

ease;

4 weeks Care4Heart

5 2015, Isetta et al.

(Isetta et al., 2017)

Evaluate the effectiveness of the

APPnea app in getting patients to

improve adherence to continuous

positive airway pressure (CPAP)

use. (Obstructive sleep apnea)

General: (n = 60); 6 weeks APPnea

6 2018, Ravn Jakobsen

et al. (Ravn Jakobsen

et al., 2018)

Evaluate the effectiveness of

the My Osteoporosis Journey

app in improving patients’ self-

management, as well as their ability

to make decisions in their therapy.

(Asymptomatic osteoporosis)

General: (n = 18); inclusion if in possession

of a smartphone, Tablet or computer; Danish-

speaking; age ≥ 50 years and Age ≤ 65 years;

suffering from asymptomatic osteoporosis with a

T-score < −2.5 in the hip or lumbar spine; exclu-

sion if previous osteoporosis-related fractures or

severe mental illness;

4 weeks My Os-

teoporosis

Journey

7 2019, Brewer et al.

(Brewer et al., 2019)

Development and evaluation of

an app for cardiovascular disease

prevention in African Americans.

(Cardiovascular diseases)

General: (n = 50) inclusion if age >= 18 years;

basic ability to use the Internet; in possession of

an Internet connection; active e-mail address; low

intake of fruits and vegetables (< 5 intakes/day);

ability to engage in physical activity; exclusion if

participant in a regular ﬁtness program; pregnant;

visually impaired; hearing impaired; mental disor-

der; participation in the app development process;

participation in the program associated with the

study;

10 weeks FAITH!

8 2018, Dillingham et

al. (Dillingham et al.,

2018)

Improving adherence to therapy

among HIV-positive people using

an mHealth intervention. (AIDS)

General: (n = 77); inclusion if newly diagnosed

with HIV; prolonged break since last treatment, or

increased risk for treatment discontinuation; pos-

session of basic literacy;

12 months Not speciﬁed

9 2017, Eyles et al.

(Eyles et al., 2017)

Evaluating the usefulness of the

SaltSwitch app in getting partici-

pants to reduce salt intake (Car-

diovascular disease (coronary syn-

drome, revascularization, angina

pectoris))

General: (n = 66); randomized with control

group; inclusion if age >= 40 years; diagnosed

with cardiovascular disease; ownership of smart-

phone; exclusion if acute cardiac event in the past

3 months; heart insufﬁciency; diagnosed with se-

vere heart disease; refusal of medical treatment;

regular use of anti-inﬂammatory agents; regular

use of prednisolone;

6 weeks SaltSwitch

10 2019, Serlachius et

al. (Serlachius et al.,

2019)

Evaluating the utility of an app in

self-care habits, disease awareness,

and engagement to reduce symp-

toms in gout patients. (Gout)

General: (n = 72); randomized with control

group; inclusion if diagnosed with gout; age ≥ 18

years; understand English; owned a smartphone

2 weeks Gout Central

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Table 2: Intervention and results of the evaluated studies.

# Intervention Measurements Results

1 Three control arms; one arm with app and health

counseling; one arm without app but with health

counseling and one control group; group with app:

Few Touch Application includes the following

features: (ﬁlling out a diary on diabetes progres-

sion; motivational feedback; monitoring eating

habits; measuring blood glucose levels; monitor-

ing physical activity; goal setting system); coun-

seling by phone by a trained nurse; group with-

out app but with health counseling: counseling by

phone by a trained nurse

Age; gender; marital status; education level;

employment; medication; HbA1c level; height;

weight; blood pressure; comorbidities; willing-

ness to improve one’s health status; eating habits;

quality of life as HRQL (health related quality of

life); knowledge about diseases as heiQ (Health

Education Impact Questionnaire); symptoms of

depression using the (The Center for Epidemio-

logic Studies Depression scale (CES-D))

HbA1c levels decreased in all groups,

including the control group; there were

no signiﬁcant group-speciﬁc differences

in HbA1c levels; the group using the

app had signiﬁcantly better scores in self-

management and disease understanding;

older participants had higher adherence to

the intervention

2 Delivery of BlueStar app on additional smart-

phone with all other features disabled; BlueStar

includes the following features: (tracking health,

blood glucose levels, and physical activity; send-

ing motivational messages to the user)

Measurement of HbA1c level; patient’s percep-

tion of his or her ability to self-manage; amount

of patient’s use of disease improvement opportu-

nities

No signiﬁcant differences between control

and intervention group in terms of HbA1c

value; also no signiﬁcant differences in

secondary outcomes

3 mVIP includes the following features: (logging

of HIV-related symptoms; receiving a symptom-

based strategy through the app to alleviate symp-

toms); participants were required to log their

symptoms at least on a weekly basis

Medication adherence; incidence of 13 HIV-

induced symptoms and their intensity; demo-

graphic background using the PROMIS-29 ques-

tionnaire

Five of 13 symptoms improved signiﬁ-

cantly in the intervention group

4 Daily SMS; 20 minute meeting; Care4Heart in-

cludes the following features: information ma-

terial on coronary heart disease; two videos on

relaxation techniques; functions for calculating

BMI, calorie intake and risk calculation for the

probability of developing coronary heart disease

Coronary heart disease questionnaire; Heart Dis-

ease Fact Questionnaire-2 (HDFQ-2) to deter-

mine patients’ knowledge; Perceived Stress Scale-

10 (PSS-10) to determine participants’ perceived

stress; behavioral Risk Factor Surveillance Sys-

tem (BRFSS) questionnaire to measure risk fac-

tors in participants; questionnaire on participants’

opinion of the study

Signiﬁcantly improved perception of coro-

nary heart disease as the second leading

cause of death in Singapore; signiﬁcantly

increased control of cholesterol; better un-

derstanding of coronary heart disease; no

improvement in lifestyle or stress percep-

tion

5 Daily reminders to answer the obstructive sleep

apnea questionnaire; APPnea includes the follow-

ing features: obstructive sleep apnea treatment

questionnaire; weekly BMI query; general health

education

Regular obstructive sleep apnea treatment ques-

tionnaire; duration of CPAP use; satisfaction with

app

Participants who used the app daily were

also signiﬁcantly more likely to use CPAP;

participants were satisﬁed with the app

6 My Osteoporosis Journey includes the following

features: (information on asymptomatic osteo-

porosis; recommendations for action; automatic

availability of scans of bones taken in the labo-

ratory; risk assessment for fractures);

Collecting data using semi-structured interviews; The app provided a sense of conﬁdence

and reassurance; the app helped making

decisions regarding therapy, as well as im-

proving patient self-management;

7 FAITH! includes the following features: informa-

tional materials on cardiovascular diseases; tests

to review what has been learned; chat to interact

with other users of the app; tracking of physical

activity and eating habits; recipes; videos of pas-

tors for motivation, since the study took place in a

religious context)

Questionnaire on general information about so-

ciodemographic characteristics, smartphone use,

sources of health-related information; level of in-

terest in learning more about health-promoting

behaviors and cardiovascular disease; discussion

with participants about preferred features; ques-

tionnaire on satisfaction with the app

Participants were very satisﬁed with the

app; participants rated the app as help-

ful in acquiring knowledge and changing

lifestyles

8 PositiveLinks app includes the following features:

tracking well-being and stress levels; medication

adherence; various quizzes; reminders for doctor

visits; shared chat for all app users; HIV informa-

tional materials; stress management techniques

General sociodemographic characteristics; CD4

cell count; viral load; consistency of physician

visits; severity of viral suppression

CD4 cell count increased on average; Vi-

ral load decreased; Consistency of physi-

cian visits increased signiﬁcantly; Com-

munity support in the app was considered

very helpful; Participants who transitioned

to continuous therapy were found to have a

signiﬁcantly higher rate of use; In general,

no correlation was found between app use

and continuous physician visits

9 Users of the app could scan food and check for

salt content; weekly reminder to use the app

Measurement of salt content of purchased goods;

energy content of goods; fat content of goods; sys-

tolic blood pressure; satisfaction with app; cost of

goods; sodium in urine

The intervention group signiﬁcantly de-

creased their salt intake compared to

the control group during the intervention

phase; there were no signiﬁcant differences

in secondary outcomes

10 Gout Central includes the following features: ed-

ucational information about gout and tips for im-

proving symptoms; tracker for uric acid levels and

gout attacks; scheduling appointments with your

doctor; tracking medication dosages; ability to

contact a health care provider with questions

Mobile Application Rating Scale (uMARS, scale:

1 to 5) to assess user self-engagement; user self-

assessment of self-care habits; self-assessment of

current disease state

Users of the gout treatment app reported

a signiﬁcantly deeper understanding of

the importance of the disease and found

it more engaging; there was no change

in participants’ self-care behaviors; Gout

Central users’ perceptions of the disease

became signiﬁcantly more negative; symp-

toms were perceived signiﬁcantly more

negatively by Gout Central users, and their

emotional attitudes toward the disease also

became signiﬁcantly more negative

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