Ambulatory Assessment of Mental Health and Well-being using an

Experience Sampling Methodology: Pipeline

Jaymar Soriano

, Alyanna Gacutan

, Martina Sophia Casiano

, Jeanne Nicole Magpantay

Allure Migy Daidelle Tanquintic

, Gina Rose Tongco-Rosario

Grazianne-Geneve Mendoza

and Christie Sio

Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines

Department of Psychology, University of the Philippines Diliman, Quezon City, Philippines

Keywords: Mental Health and Well-being, Ambulatory Assessment, Experience Sampling Methodology, Software

Development, Application Programming Interface.

Abstract: Mental health disorders are prevalent in our society today as they affect one in four people all over the world,

according to the World Health Organization. This necessitates a proactive method of mental health assessment.

Clinical assessments and paper-and-pen reporting are usually done through retrospective reports, which are

subject to memory bias. With the advancement in technology and smartphones becoming an inherent and

integral part of day-to-day life, ambulatory assessment of mental health and well-being would be greatly

improved. In this study, we analyze different methods of mental health assessment, their respective benefits

and drawbacks, and from which we propose a pipeline based on Experience Sampling Methodology (ESM).

The pipeline is composed of a web application for therapists and a mobile application for patients. The

therapist creates ESM-based assessments to their patients using the web application that communicates with

the mobile application through an application programming interface. This pipeline aims to overcome

retrospective biases in assessing the patient’s mental health and well-being by using more reliable behavioural

patterns from the data. Sophisticated data encryption may be utilized to ensure patient-therapist confidentiality.

The same system is also designed to be used by psychologists to send ESM-based surveys to their intended

participants and perform statistical analysis from the respondents’ data, allowing improved data security for

the respondents. With this capability, generation of data would be faster and safer, and more research can be

done to improve and accelerate analysis and diagnosis of mental health and well-being.

1 INTRODUCTION

Mental health problems are a serious and growing

issue among adolescents and adults worldwide. The

World Health Organization (WHO) reports that 1 out

of 4 people in the world, with 10-20% of children and

adolescents, experience mental health problems.

WHO adds that anxiety and depression are the most

common causes of disability in adolescents and at

worst can lead to self-harm and suicide. Severe

mental health problems include depression, anxiety

disorders, bipolar disorders, eating disorders,

schizophrenia, dementia, developmental disorders,

and substance use (Lake & Turner, 2017). Current

medications and treatment models are insufficient to

treat the complexities of mental health problems

https://orcid.org/0000-0001-9647-5999

mainly because it is underreported worldwide and

lacks research. Other factors that stifle progress on

mental health include lack of sufficient funds and

existing societal prejudices.

The Philippines has recently passed its Mental

Health Act in 2018 (Republic Act No. 11036) that

aims to enhance mental health services and promote

the safety of mental health patients. Universities and

other institutions are taking the initiative to be more

understanding of mental health problems and because

of this, intervention and counselling is becoming

commonplace. However, mental health remains

underfunded, inaccessible, and oftentimes

stigmatized in the country.

In adolescent population, depression is often

prejudiced to be harmless and a normal part of their

Soriano, J., Gacutan, A., Casiano, M., Magpantay, J., Tanquintic, A., Tongco-Rosario, G., Mendoza, G. and Sio, C.

Ambulatory Assessment of Mental Health and Well-being using an Experience Sampling Methodology: Pipeline.

DOI: 10.5220/0010271405330540

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 533-540

ISBN: 978-989-758-490-9

533

growth (Lee, et al. 2013). This is problematic as it

ignores the dangers caused by not only depression but

mental illnesses in general. Mental disorders are

prevalent among adolescents, especially college

students who spend most of their time in schools.

College is a microcosm of society where it

encompasses all activities, social relationships, and

health conditions and is a challenging period in an

adolescent’s transition to adulthood. Hunt and

Eisenberg (2010) reported that financial status, social

support, trauma, and academic stressors are some of

the factors that affect college students’ mental health.

Filipino students who frequently drink and smoke,

lived away from parents, and are unsatisfied with

their financial condition had higher depressive

symptoms while students who have high levels of

closeness with their peers and families tend to have

lower levels of depressive symptoms (Lee, et al.

2013). A study by Cleofas (2020) shows that there is

an increase in student suicide cases in recent years

caused by social and academic factors to be addressed

by social institutions (e.g., family, school, policy-

making bodies). Universities have been taking steps

to address mental health issues and protect students’

well-being. The University of the Philippines

Diliman has taken an initiative to address mental

health problems within the university by establishing

The UP Diliman Psychosocial Services (UPD

PsycServ) to promote mental health and well-being

among students, faculty, and staff (Santos 2020).

Lake and Turner (2017) expressed that “mental

illness is the pandemic of the 21

century and will be

the next major global health challenge”.

Countermeasures are being demanded to prevent,

avoid, and alleviate the consequences of these mental

health problems.

2 MENTAL HEALTH

ASSESSMENT

2.1 Traditional Methods

In assessing mental health, clinical experts often

provide questionnaires to their patients in order to

gather information about them. During therapy

sessions, therapists also ask their clients about their

experience over the past days or weeks. Normally,

patients are given a diary form that contains their

feelings, thoughts and experiences on each day so that

they can monitor themselves and provide more

accurate data for the therapists. These methods lack

process-mediating aspects and context sensitivity

since they only focus on outcome measures.

2.1.1 Questionnaires

Questionnaires are the most common tool to collect

self-report data from the study participants. They can

contain close or open questions, or both. Close

questions provide limited choices whereas open

questions allow the participants to answer using their

own words. Questions also involve response or rating

scales where respondents usually express how

strongly they agree or disagree with a particular

statement.

Self-report questionnaires are frequently used in

clinical assessments to identify signs and symptoms

of psychological problems (Demetriou, et al. 2015).

They serve as a screening tool that can help health

care practitioners in their planning of treatment

process for their patients. For instance, Beck

Depression Inventory is a 21-item multiple choice

self-report questionnaire widely used to measure the

severity of depression. These questionnaires may be

insufficient when diagnosing patients but are

necessary for the assessment of their experiences

(Demetriou, et al. 2015). Furthermore, retrospective

questionnaires are used in feedback procedures of

psychotherapy. Therapists often ask their clients to

answer questionnaires in order to monitor the

progress of the treatment. However, this method lacks

information about the experiences in everyday life

since the feedback forms are completed only before

or after the therapy session (Schiepek, et al. 2016).

2.1.2 Diary

Self-report diary is a tool that contains intensive and

repeated reports of person’s thoughts, feelings,

moods, pains or experiences near the time they occur.

Diary methods increase the ecological validity of data

as they capture the experiences of participants on a

daily basis (Iida, et al. 2012). Unlike retrospective

reports, diaries or frequent assessments are useful in

examining psychological processes that are

susceptible to rapid fluctuations (Schiepek, et al.

2016). This allows experts to observe how much the

experiences of an individual vary over time and

identify the events that might cause the changes (Iida,

et al. 2012). Since a diary report is close to the time

when the experience occurs, it reduces memory recall

and retrospective biases, thus producing more reliable

and accurate data (Stone, et al. 2003).

Traditional diary methods often involve pencil-

and-paper system in which participants are provided

with booklets of questionnaires, one for each diary

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entry. The questionnaires in diaries are similar to

typical survey questionnaires but they are shorter to

reduce participant burden. Despite the advantages,

diary methods also have drawbacks. Participants have

to remember and complete diary entries in a timely

manner. If participants fail to fill out a diary entry and

complete it at a later date, the efficacy of diary

methods will be undermined (Stone, et al. 2003). This

makes the pencil-and-paper diary system more

problematic as the examiners cannot track the

compliance of the participants (Iida, et al. 2012). In

this case, there is no guarantee that the participants

are actually completing a diary entry each day. On the

other hand, electronic diaries automatically record the

date and time of each entry which allows researchers

to determine if the entries were made on time or not.

In addition, an electronic diary may have an alarm to

remind the participants to respond to their diary

entries. To record the compliance with pencil-and-

paper diary methods, patients were given a diary

embedded with sensors to keep track of date and time

of the entries. The study by Stone, et al. (2003) found

that most of the entries were fake or completed at later

dates. The actual compliance rate in paper diaries was

only 11% whereas electronic diaries produced a

remarkably high compliance rate of 94%. According

to the authors, the electronic diaries’ features such as

alarms may have an effect in the participants that urge

them to comply with the daily protocol of self-report.

2.2 Ambulatory Assessment

Ambulatory assessment (AA) is a vital research tool

used to investigate psychological, emotional,

behavioral, and biological processes of individuals in

their daily life. It encompasses various methods that

aid in studying people in their natural environment,

including momentary self-report, observational, and

physiological (Trull & Ebner-Priemer 2014). Its goal

is to minimize retrospective biases while gathering

ecologically valid data from patients’ everyday life in

real time or near real time. The major characteristics

of AA include investigation of mechanisms and

dynamics of symptoms, prediction of future

recurrence or onset of symptoms, monitoring of

treatment effects and prediction of its success, and

prevention of relapse (Trull & Ebner-Priemer 2013).

Through the advancement of technology,

smartphone-based monitoring of objective and

subjective data in mood disorders has become a

rapidly growing approach and research field (Dogan,

et al. 2017). This has paved the way for methods like

ambulatory assessment and experience sampling

method to collect real-time user data more efficiently

and accurately. This breakthrough has improved

ecological validity of laboratory results through

combined lab-field studies, and investigating gene-

environment interactions (Trull & Ebner-Priemer

2013). Although promising, a much larger evidence-

base study is necessary to fully assess the potential,

as well as the risks, of such approaches. A discussion

of acceptability, compliance, privacy, and ethical

issues is yet to be concluded (Dogan, et al. 2017).

2.2.1 Experience Sampling Methodology

ESM is generally regarded as the “gold standard” for

studies of emotions to acquire unambiguous

information about participants’ momentary feelings in

vivo et in situ. One of the ways by which data collection

is accomplished is through the use of smartphone

applications or applications specifically designed to

accommodate the experience sampling methodology.

As a research methodology, ESM has become

widely recognized in a variety of fields in and out of

psychology as an important tool in collecting data

about different aspects of people’s everyday

experiences. Its potential has particularly been

capitalized through the development of ESM

applications that enable ease of data collection. For

instance, research on the use of ESM apps in the field

of mental health is proliferating (Verhagen, et al.

2016, Wichers, et al. 2011). ESM apps have been

increasingly used for delivering psychological

interventions, as well as monitoring symptoms of

people with physical illnesses. ESM apps have also

been used in investigating topics related to education

(e.g., academic performance, student motivation),

industrial/organizational psychology (e.g., employee

performance, engagement, and well-being and

workplace behavior), tourism (e.g., tourist

experience), and communication studies (e.g., mass

media and social media use) and many others.

As of late, available ESM apps for research are

foreign-made and a one-year subscription costs an

average of $1,500. While participants of ESM studies

may download the apps for free, researchers must

purchase a subscription package to avail the services

of the app and web-based program. As such, the

development of a locally designed ESM app may

strongly encourage other Filipino researchers to

pursue ESM-oriented studies and pave the way for

new avenues to explore in research in the Philippines.

2.2.2 Mental Health Applications

Over the recent years, several mental health

applications (mHealth apps) have been made

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535

available to smartphone users. While definite

evidence for successful outcomes cannot be assured,

mHealth apps offer a big potential in the future of

mental health care. The review and evidence-based

recommendations for future developments made by

Bakker et al. (2016) state the lacking features of these

apps that would greatly improve their functionality or

include features that are not optimized. Furthermore,

mHealth apps developers rarely conduct or publish

trial-based experimental validation of their apps.

Indeed, a previous systematic review revealed a

complete lack of trial-based evidence for many of the

hundreds of mHealth apps available.

Another review concerning mHealth apps in

psychotherapy context by Lui, et al. (2017) stated that

mobile apps, especially in this generation, are of

particular interest as they can be a platform for

dissemination of interventions. These apps are also

valuable when applied and used in a clinical context

as they “can aid symptom assessment, provide

psychoeducation, track treatment progress, provide

real-time intervention and communication, and can

take advantage of game technologies, global

positioning system (GPS), and connectivity to

external devices such as biofeedback sensors” (Lui, et

al. 2017, Luxton, et al. 2015). Using these features

may lead to better clinical outcomes as these promote

user engagement, interaction, and motivation. Given

that global mobile phone penetration reached 91% at

the end of 2012 with 4.3 billion unique mobile

subscribers identified, mobile health apps supported

by mobile devices can thus be delivered to a large

number of people worldwide. A study by Donker, et

al. (2013) revealed that nearly 70% of patients

expressed interest in using mobile health apps to self-

monitor and self-manage their mental health. They

claim that early evidence suggests that patients open

up more while using an app compared to face-to-face

therapy. Another study by Chandrashekar (2018)

tried to investigate the effectiveness of these mHealth

apps, which use cognitive behavioral therapy (CBT),

mindfulness training, mood monitoring, and

cognitive skills training to treat depressive symptoms.

The author reported that using apps to alleviate

symptoms and self-manage depression significantly

reduced patients’ depressive symptoms compared to

control conditions (p<0.001).

3 ESM-BASED PIPELINE

Self-report methods such as the use of retrospective

questionnaires and daily diaries have limitations

when assessing one’s mental health. This study aims

to overcome these shortcomings of current

assessment methods in clinical trials.

 Manual Data Collection - Paper-based surveys

remain convenient for researchers as they are

not required to be familiar with software

programs to generate their questionnaires.

However, data collection using paper forms can

be time consuming and susceptible to data entry

errors.

Solution - Users can easily create surveys using

the web app that allows easy access to data as

soon as responses are submitted. monitor the

results. They can view summary responses for

each question as well as individual answers. The

application also includes a dashboard that

allows them to view trend in number of

responses for each day.

 Memory Biases - In clinical trials, practitioners

often ask their patients to report about their past

experiences. However, retrospective recall is

found to be unreliable as it introduces memory

biases (Stone, et al., 2003). Interviews and

retrospective questionnaires focus entirely on

outcome measures that can be ineffective when

monitoring psychological processes such as

moods which are prone to sudden changes.

Experts began to use diary methods such as

ESM that involves repeated assessments over a

period of time.

Solution - Users can repeatedly prompt

participants to complete self-report

questionnaires in their smartphones. While this

method may involve anonymous respondents,

users can also create client accounts so they can

view the names of the participants when

monitoring the survey results. The web app is

shall aid practitioners to conduct ESM and

improve treatment processes where patients can

learn to manage their mental states and

behaviors.

 Burden to Participants - Despite the

challenges, ESM has its drawbacks. The

investment of time and effort required in

repeated assessments can lower the participant

compliance. In addition, implementing ESM

where participants are frequently prompted to

answer questions could interrupt their ongoing

activities resulting in noncompliance with the

assessment.

Solution - Therapists can specify a time frame

and the number of times in which a module is

available to the participants’ smartphone. This

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allows control on the timing elements of their

questionnaires.

The proposed ESM-based pipeline for ambulatory

assessment is composed of a web application for

therapists and a mobile application for patients. The

therapist creates ESM-based assessments to their

patients using the web application that communicates

with the mobile application through an application

programming interface (API). The same system is

also designed to be used by psychologists to send

ESM-based surveys to their intended participants and

perform statistical analysis from the respondents’

data.

3.1 Web Application

The ESM web application aims to support the needs

of psychologists and psychiatrists as they build their

own questionnaires. It allows creating, deleting,

editing, and viewing of questionnaires and

participants. It also interprets data collected into

visual representations. Figure 1 shows the use cases

of the web application for therapy use and

psychological surveys for intended participants.

The user interface of the web app consists of the

following:

 Log-in Page - where the user enters their

credentials to gain access to the website. First-

time users also have an option to sign-up.

 Dashboard - shows visual summaries of the

user’s surveys (Figure 2).

 Surveys Module - the main content of the

application. It has two modes: view and create.

View mode displays a survey’s data while create

mode allows the user to customize their own

survey. The app allows several input response-

types such as checkbox, date and time, number

wheel, numerical, option, photo upload, rating,

slider, text, and website link.

 Clients Module - allows the user to manage the

list of clients. The user can add, delete, and view

a client’s data.

3.2 Mobile Application

According to a website called statcounter, a web

analytics service tracking more than two million sites

globally, 81.91% of the Mobile Operating System

Market Share in the Philippines from May 2019 to

May 2020 are Android. Since the majority of the

targeted users are using Android-powered mobile

devices, the initial application shall run with the said

operating system. Moreover, the app requires

internet, camera, and external storage writing

permissions. It has been heavily developed and tested

on an Android version 10 smartphone with 6.26-inch

touchscreen display, 1080x2340 pixels of resolution,

pixel density of 412 pixels per inch (ppi) and an

aspect ratio of 19.5:9. The mobile app also uses a

local storage implementation to save user

questionnaire progress, user preferences, app settings,

and other necessary data that may be viewed offline.

Overall, pastel colors were chosen to display the

whole UI as we wanted to achieve a calming and

soothing effect for our app. According to an article

which explains how to effectively use color in

treatment facilities, pastel colors are optimal for

treatment centers as they are soft and comforting

(Hoisington 2017). Thus, we decided to incorporate

these colors for our scheme.

Furthermore, the surveys were designed

differently from the usual format of web surveys. A

study done comparing data from web surveys and

chatbots shows that chatbots encourage user

engagement and produce higher quality data from the

users (Kim, et al. 2019). Thus, we opted to have the

surveys in chatbot form so that it will be more

interactive for the users and encourage them to

answer these surveys and still get quality data, even if

it is required of them to answer these for a certain

amount of time.

The user interface of the web app consists of the

following:

Log-in Page – users are required to enter

credentials given to them by the admin. Once

successfully logged in, an authentic token shall be

returned to the mobile app. This token shall serve as

the “key” to access the user-assigned surveys,

questionnaires, and account settings.

Figure 1: Use cases for ESM web app.

Ambulatory Assessment of Mental Health and Well-being using an Experience Sampling Methodology: Pipeline

537

Figure 2: User interface of the web app.

Figure 3: Log-in and home screen of mobile app.

Figure 4: Weekly and monthly mood charts.

 Home - displayed after successful login. The

topmost part of the screen features a daily mood

selector which enables the users to input their

current feeling for the day (Figure 3). The moods

to be chosen from are “great”, “good”, “meh”,

“sad”, “angry”. The mood chart below the selector

will automatically update upon selection and will

display the user’s mood trend for the week. Users

can also view a monthly tally of their mood as

shown in Figure 4.

 Therapy/Research Module - the main content of

the application. The research module contains all

the queued surveys that are specific to the user for

answering. Each survey has a progress bar

showing the user’s progress, visually and in

percentage. The user will also be able to add

surveys they wish to participate in by entering

survey codes provided by the facilitators of the

research. This is to ensure that the surveys are by

invitation-only and that respondents can be easily

monitored. The next button then leads to the

survey form proper. The survey also has the same

input response types from the used in the web app

such as checkbox, date and time, number wheel,

numerical, option, photo upload, rating, slider,

text, and website link. The therapy module has the

same features as the research module except that

it is only available to users who are registered as

presently undergoing treatment or counselling.

3.3 API and Databases

The web application is designed to exchange data

with the mobile application through an application

programming interface (API) that manages the

database stored in the cloud (Figure 5). The database

uses a relational structure using MySQL. The API

implements token-based authentication with JSON

Web Tokens (JWT) to ensure that every request is

sent from a verified user. When users log in, the API

checks if they are registered to the app. Once the user

is authenticated, the API will generate a token and

return it to the user. The token contains the user’s

information (e.g., username), the hashing algorithm

used to sign the token, and the signature. Users should

include this token in the HTTP authorization header

for every request so that the API can verify them.

3.4 Pipeline Demonstration

To demonstrate that the system works, the Dialectical

Behavior Therapy (DBT) Module is constructed in

the web app. The module aims to help the patients to

monitor their behaviors and eventually learn to

manage them. The DBT Card from Valley Health

shown in Figure 6 is commonly used by therapists for

assessment of their patients in a weekly basis. An

interactive ESM version of this was created as shown

in Figure 7. This is then communicated to the

patient’s mobile app via an API and is displayed as a

Therapy module (Figure 8). The patient’s records are

easily accessed and displayed in a table (Figure 9).

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Figure 5: Structure of the ESM-based pipeline.

Figure 6: DBT Diary Card [valley-health.org].

Figure 7: DBT Module using the web app.

4 CONCLUSION

Over time, the use of smartphones has been a constant

and a necessity to most people especially to college

students. But with the rise of technology also came

the escalation of mental health disorders. As such,

therapeutic and research activities conducted online

through mobile applications have also been an option

to catch up with the increasing mental health

demands. Such use of technology may prove to be

beneficial to individuals who seek professional help,

as well as to the researchers who aim to study these

mental health concerns using more reliable data

offered by ESM.

This paper aims to piece together an effective tool

to bridge these concerns at least in a local context. An

integrated web and mobile applications using an ESM

pipeline is developed and is planned to be proposed

to PsycServ to cater to increasing student demand at

the University of the Philippines. The same system

will be deployed to the university’s Department of

Psychology and is hoped to be used for ESM-based

studies of mental health and well-being of students

and other intended participants. This will pave wave

to generation of data that will be used for researches

and studies that could accelerate analysis and

diagnosis of mental disorders. Moreover, the

availability of user data, albeit anonymized, can be

studied using machine-learning methods and from

which artificial intelligence capabilities of the mobile

app may be developed.

Figure 8: DBT Module screens using the mobile app.

Ambulatory Assessment of Mental Health and Well-being using an Experience Sampling Methodology: Pipeline

539

Figure 9: DBT patient response page via the web app.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. D.M. Quinones, Dr.

A.J. Galang, and Dr. A. Tuazon for the helpful insights and

discussions on this project.

REFERENCES

Lake, J., & Turner, M. S. (2017). Urgent need for improved

mental health care and a more collaborative model of

care. The Permanente Journal, 21.

Lee, R. B., Maria, M. S., Estanislao, S., & Rodriguez, C.

(2013). Factors associated with depressive symptoms

among Filipino university students. PloS one, 8(11),

e79825.

Hunt, J., & Eisenberg, D. (2010). Mental health problems

and help-seeking behavior among college students.

Journal of adolescent health, 46(1), 3-10.

Cleofas, J. V. (2020). Student involvement, mental health

and quality of life of college students in a selected

university in Manila, Philippines. International Journal

of Adolescence and Youth, 25(1), 435-447.

Santos, G. P. (Jan 2020). UP Diliman struggles to meet

students’ rising mental health demands. Retrieved from

https://onenews.ph/up-diliman-struggles-to-meet-stud

ents-rising-mental-health-demands on January 31,

2020.

Demetriou, C., Ozer, B.U. and Essau, C.A. (2015). Self‐

Report Questionnaires. In The Encyclopedia of Clinical

Psychology (eds R.L. Cautin and S.O. Lilienfeld).

Schiepek, G., Aichhorn, W., Gruber, M., Strunk, G.,

Bachler, E., & Aas, B. (2016). Real-time monitoring of

psychotherapeutic processes: concept and compliance.

Frontiers in psychology, 7, 604.

Iida, M., Shrout, P. E., Laurenceau, J. P., & Bolger, N.

(2012). Using diary methods in psychological research.

Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E.,

& Hufford, M. R. (2003). Patient compliance with

paper and electronic diaries. Controlled clinical trials,

24(2), 182-199.

Trull, T. J., & Ebner-Priemer, U. (2014). The role of

ambulatory assessment in psychological science.

Current Directions in Psychological Science, 23(6),

466-470.

Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory

assessment. Annual review of clinical psychology, 9,

151-176.

Dogan, E., Sander, C., Wagner, X., Hegerl, U., & Kohls, E.

(2017). Smartphone-based monitoring of objective and

subjective data in affective disorders: where are we and

where are we going? Systematic review. Journal of

medical Internet research, 19(7), e262.

Verhagen, S. J., Hasmi, L., Drukker, M., van Os, J., &

Delespaul, P. A. (2016). Use of the experience sampling

method in the context of clinical trials. Evidence-based

mental health, 19(3), 86-89.

Wichers, M., Simons, C. J. P., Kramer, I. M. A., Hartmann,

J. A., Lothmann, C., Myin‐Germeys, I., ... & Van Os, J.

(2011). Momentary assessment technology as a tool to

help patients with depression help themselves. Acta

Psychiatrica Scandinavica, 124(4), 262-272.

Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N.

(2016). Mental health smartphone apps: review and

evidence-based recommendations for future

developments. JMIR mental health, 3(1), e7.

Lui, J. H., Marcus, D. K., & Barry, C. T. (2017). Evidence-

based apps? A review of mental health mobile

applications in a psychotherapy context. Professional

Psychology: Research and Practice, 48(3), 199.

Luxton, D. D., June, J. D., & Chalker, S. A. (2015). Mobile

health technologies for suicide prevention: feature

review and recommendations for use in clinical care.

Current Treatment Options in Psychiatry, 2(4), 349-

362.

Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.

R., & Christensen, H. (2013). Smartphones for smarter

delivery of mental health programs: a systematic

review. Journal of medical Internet research, 15(11),

e247.

Chandrashekar, P. (2018). Do mental health mobile apps

work: evidence and recommendations for designing

high-efficacy mental health mobile apps. Mhealth, 4.

Mobile operating system market share Philippines.

Retrieved from https://gs.statcounter.com/os-market-

share/mobile/philippines on May 25, 2020.

Hoisington, A. How to effectively use color in treatment

facilities, Aug 2017. Retrieved from https://www.

psychcongress.com/article/facility-architecture/how-

effectively-use-color-treatment-facilities on January

31, 2020.

Kim, S., Lee, J., & Gweon, G. (2019, May). Comparing

data from chatbot and web surveys: Effects of platform

and conversational style on survey response quality. In

Proceedings of the 2019 CHI Conference on Human

Factors in Computing Systems (pp. 1-12).

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