Technological Solution to Optimize the Alzheimer’s Disease Monitoring

Process, in Metropolitan Lima, using the Internet of Things

Katherine Jorge-L

evano, Victor Cuya-Chumbile and Willy Ugarte

Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru

Keywords:

Alzheimer’s, Internet of Things, Wearable, Health Monitoring.

Abstract:

The use of information technologies (IT) in the health sector has allowed to optimize monitoring processes

for diseases such as Diabetes or Parkinson’s. For this reason, the incorporation of IT into the monitoring of

neurological diseases, such as Alzheimer’s, will enable remote monitoring solutions on the patient’s health.

This study will develop a mobile and web application that will monitor, through an IOT device, changes in the

patient’s vital signs (oxygenation and blood pressure), impairment of cognitive functions (memory, calculation

and concentration) and the patient’s sleep status with Alzheimer’s. In addition, with this solution the patient’s

doctor will be able to record recommendations on the progression of Alzheimer’s disease. The study was

validated with 3 physicians and 3 caregivers, who participated in the validation process by comparing the time

before and after using the technology solution. As a result, the optimization of the monitoring process has been

validated, allowing real-time control of the progress of the disease and having the appropriate considerations

of the doctor in case of any incident that may happen with the patient

1 INTRODUCTION

According to the Peruvian College of Physicians

(CMP), it is estimated that approximately 200,000 Pe-

ruvians could currently suffer from Alzheimer’s dis-

ease, and the ﬁgure is estimated to be quintupling by

early 2050 (CMP - https://bit.ly/3lmkHEF).

Alzheimer’s is a disease that increases with age,

starting with subtle symptoms such as memory prob-

lems, disorientation in time/space, and difﬁculty nam-

ing objects. The World Health Organization (WHO)

classiﬁes the signs and symptoms of early-stage,

intermediate- and late-stage dementia. The early

stage; often goes unnoticed, presenting as more com-

mon symptoms the tendency to forgetfulness, the loss

of the notion of time and spatial desubication (WHO

- https://bit.ly/35lG6Z7).

As dementia evolves into the intermediate stage,

signs and symptoms become more apparent. In view

of this, in order to have greater knowledge about the

impact of this disease, it was proceeded to analyze

the information presented by radio program of Peru

(PTR), in which they indicated that in the Ministry of

Health in 2015 alone the number of Peruvians with

Alzheimer’s were 3,207; in 2016, from 3,309 to 2017

https://orcid.org/0000-0002-7510-618X

increased to 3,665, among new and old cases of peo-

ple over the age of 60 (RPP - https://bit.ly/2UuZXiB)

from Figure 1.

Figure 1: Indicator of people over the age of 60 diagnosed

with Alzheimer’s according to the Ministry of Health of

Peru.

This information shows the importance of incur-

ing monitoring the progression of Alzheimer’s dis-

Jorge-Lévano, K., Cuya-Chumbile, V. and Ugarte, W.

Technological Solution to Optimize the Alzheimer’s Disease Monitoring Process, in Metropolitan Lima, using the Internet of Things.

DOI: 10.5220/0010422501790186

In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 179-186

ISBN: 978-989-758-506-7

179

ease, in order to support the monitoring of patients

with the aforementioned disease.

Faced with this need to ﬁnd some kind of tech-

nological solution, a study conducted in Catania and

Messina, Sicily, developed a telemedicine system that

involves three approaches: nutritional status, biomet-

ric data monitoring and cognitive status; focused on

the care of the elderly in order to prevent neurodegen-

erative diseases. This system uses a device with inter-

net access, which contains a web platform on which

consultations are made to a doctor.

In addition, this system allows to provide cogni-

tive training sessions to the patient and all the col-

lected data are sent to the corresponding medical units

to assess the patient’s situation. At the end of the val-

idation of this project, an improvement was observed

regarding the nutritional status of the person, cogni-

tive functions and the execution of daily activities of

the participants (Maresca et al., 2019). By identify-

ing this type of solution, it has been validated what to

know about the health status and impairment of a pa-

tient’s cognitive functions makes it easier for the doc-

tor to generate accurate recommendations regarding

the advancement of the patient’s disease.

For this reason, this study answers the following

question What technology is available, what techno-

logical devices should be taken into account to de-

velop a mobile and web application that optimizes

the monitoring of Alzheimer’s disease, in the city of

Lima, Peru? In response, it is proposed to develop a

technological solution that allows to optimize the pro-

cess of monitoring Alzheimer’s disease, in Metropoli-

tan Lima, using the Internet of Things.

The main contributions in this solution:

• it allows to generate a constant communication

between doctor and caregiver,

• establish a medical control without moving the

patient to the medical center,

• provide real-time information of blood pressure,

oxygenation, the generation of alerts to the doctor

about the signs of health of the patient.

Finally, this research is structured as follows: in

Section 2, brief deﬁnitions of the concepts used in

the development of the project will be indicated, Sec-

tion 3 will include the analysis, design and construc-

tion of the project, in Section 4 the comparison will

be made between technological solutions developed

against our proposed proposal, Section 5 will explain

what has been done at the programming level, vali-

dation and results, ﬁnally Section 6 will present the

conclusions and recommendations.

2 RELATED WORKS

During the development of our proposal, various con-

tributions from studies were considered as references

such as (Rostill et al., 2018), in which authors indi-

cated that including IoT devices for home monitor-

ing and vital signs, allow to lead to important inter-

ventions in which serious complications could occur

without the use of technology.

Furthermore, in (Alexandru and Ianculescu,

2017), the authors present another assisted technol-

ogy that helps people affected by dementia, such as

the MSI-MDD digital platform.

Nevertheless, it is also evidenced that the authors

of (Jain et al., 2018), through a mobile application

evaluate and improve the emotional state of care-

givers, allowing to optimize the stimulation process

for the caregiver, applying medical metrics.

Finally, in (Gilson et al., 2019), the authors

showed that web-based video services can be used

on tablets, allowing to improve mood in people living

with dementia and improve perceptions of caregivers

about the daily interaction that older adults may have

with their caregivers.

It should be noted the works aforementioned fo-

cus on improving the mood of patients and caregivers,

however, the solutions presented do not allow to es-

tablish a medical control to monitor the disease of the

patient with Alzheimer’s, through the capture of sleep

state data and vital signs, which will allow to gener-

ate constant communication between the doctor and

the caregiver that empowers them to make decisions

about the patient’s health.

3 METHOD

The development of the project was divided into 2 of

the main dimensions of design thinking: desirability,

which will be explained in the analysis and feasibility

section, which will be explained in the design section.

3.1 Analysis

In this phase, an initial survey of the problem was car-

ried out, focusing it on the ﬁrst dimension of design

thinking: desirability.

• The ﬁrst step consisted of sharing surveys with

three doctors and ten caregivers of patients with

Alzheimer’s disease (AD), in order to obtain the

relevant data during the AD monitoring process.

In this survey, the relevance and frequency of tak-

ing vital signs of Alzheimer’s patients was evalu-

ated. The ﬁnal result made it possible to identify

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180

Figure 2: Architecture of integration of the proposed technological solution.

that blood pressure, oxygenation level, sleep sta-

tus and cognitive functions are the relevant data.

• As a second step, through the testimonies ob-

tained, we proceeded to develop the customer

journey of the patient with Alzheimer’s in its

three phases of the disease, in which the moments

through which the patient passes in their day to

day were obtained as a result, identifying eight

moments, which are: grooming, dressing, eating,

cognitive entertainment, relaxation, rest, medica-

tion and physical activity. The development of the

Customer Journey also made it possible to iden-

tify the actors in the monitoring process, which

are: doctor and caregiver, while, on the other

hand, the costs and times associated with the mon-

itoring process were identiﬁed.

• In the third step, the wearables available for data

collection were evaluated. Therefore, the evalua-

tion was carried out based on the information col-

lected in the previous steps and on the device char-

acteristics such as water resistance, cost, battery

life, built-in sensors and weight.

• The fourth step is the value proposition canvas

was developed which allowed to reafﬁrm the ini-

tial approach of the project, through the identiﬁca-

tion of gain creators, pain relievers, gains, pains.

Finally, the wearable Smartband S5 was selected

(S5 Bluetooth Heart Rate Fitness Smart Bracelet -

https://bit.ly/36nQfEc). This device allows the cap-

ture of data such as heart rate, oxygenation level,

blood pressure, step counter, calories and sleep status.

The wearable’s margin of error is 10% in case the per-

son or patient who uses it is in constant motion . This

value was indicated to the physicians who carried out

the initial survey and they said that the margin would

not signiﬁcantly affect the values obtained from the

patient, since they don’t usually perform high physi-

cal activity

3.2 Design

For the design phase, a logical and physical architec-

ture was developed in order to identify the relation-

ships between the project components. Furthermore,

the development of a mobile application that would

interact with the wearable and a web application was

established.

In addition, the interface design was developed

based on the requirements deﬁned in the analysis

phase (see Figure 2). Likewise, an integration archi-

tecture was developed to identify the relationships be-

tween the participants (doctor, caregiver and patient),

processes (patient monitoring) and information (pa-

tient health report) that compose the technological so-

lution.

Additionally, the interface design was developed

based on the requirements identiﬁed in the analysis

phase.

Technological Solution to Optimize the Alzheimer’s Disease Monitoring Process, in Metropolitan Lima, using the Internet of Things

181

3.3 Elaboration

During this stage, the applications that compose part

of the technological solution were developed: mobile

and web.

On the one hand, the coding of the mobile appli-

cation included the libraries android-smartband-sdk-

sxrblejy2aidl-release.aar and android-smartband-sdk-

sxrblejy2library-release.aar to establish the connec-

tion with the wearable Smartband S5. Likewise, this

library allowed obtaining data related to vital signs

(arterial pressure and blood oxygenation) and sleep

status. Additionally, stimulation games aimed at the

following cognitive functions were included: mem-

ory, concentration and calculation. The data related

to vital signs, sleep status and scores in the cognitive

stimulation games were stored in the MongoDB At-

las database and the visualization functionality was

integrated through reports with the support of the

AAChartCore-Kotlin library.

On the other hand, the code of the web applica-

tion includes the development of functionalities for

displaying reports related to the patient’s health sta-

tus with support of the Chart JS library. Likewise, the

functionality of recording recommendations for each

patient was incorporated (see more details in the demo

video).

4 EXPERIMENTS

For validating the system for patients with AD, sur-

veys were conducted focused on evaluating the im-

pact on time and costs in the monitoring process.

4.1 Experimental Protocol

A virtual meeting was held with a doctor and two

caregivers to consult on the functionalities of the

web and mobile solution, it should be noted that be-

fore starting the meeting a pre-validation survey was

shared, which allowed obtaining the indicators related

to costs and time that involves monitoring the disease.

After presenting the solution, insights were ob-

tained about the system and with a post-validation

survey the information was collected to identify the

variation in the initial indicators. Likewise, the same

dynamic was carried out with two additional care-

givers, but in this case, the virtual meeting was re-

placed with a demonstration video (see more detail in

this meeting).

4.2 Experimental Results

The focus of the validation is analyzing the results ob-

tained in the pre-validation and post-validation survey

that was carried out as a ﬁnal step of the desirability

dimension. The results of the aforementioned surveys

allowed to verify the reduction of costs and time re-

lated to the monitoring of Alzheimer’s disease (see

more detail of these surveys).

The following is what has been identiﬁed with re-

spect to cost reduction (see Figure ??):

• Initial Phase: it was identiﬁed that 50% of

the people surveyed in the pre-validation, indi-

cated as costs related to the monitoring process

of Alzheimer’s disease, the transfer to the med-

ical center. However, in the post-validation this

percentage was reduced to zero, leaving only the

costs related to the care service (see Figures 3a

and 3b). The reduction in the mentioned cost

was achieved by the use of telemedicine, which

doesn’t require a constant physical transfer by the

patient to the medical center.

• Moderate Phase and Severe Phase: it was iden-

tiﬁed that, on average, 38% of the people sur-

veyed in the pre-validation indicated as costs re-

lated to the monitoring process of Alzheimer’s

disease two main sources: a) emergency services

and b) transportation costs to the medical Center.

However, in post-validation, this percentage was

reduced to 29% (see Figures 3c, 3d, 3e and 3f).

The reduction of the cost of the emergency service

was achieved due to the fact that there is constant

monitoring of the patient’s health by the doctor,

who gets daily information about the variation of

the patient’s vital signs. This allows the doctor to

make immediate decisions about the medications

that the patient may require.

Nevertheless, the reduction in the cost of trans-

portation to the medical center, as in the

mild phase, was achieved due to the use of

telemedicine, since the doctor has the information

required for the evaluation of the patient.

Regarding the reduction in the time of the tasks

associated with the monitoring of Alzheimer’s (see

Figures 4,5,6,7 and 8), it was identiﬁed that various

tasks are usually performed in a range greater than 20

minutes, it should be noted that, when using mobile

app, web app, and wearable, task time is reduced by a

maximum of approximately 19 minutes in total.

• Measurement of Blood Pressure and Oxygena-

tion: we identiﬁed that this task (see Figure 4)

was previously performed between 5 to 10 min-

utes (80% of the time) and between 21 to 60 min-

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(a) Pre-validation (Initial) (b) Post-validation (Initial)

(e) Pre-validation (Severe) (f) Post-validation (Severe)

Figure 3: Pre- and post-25cm.

utes (20% of the time) (see Figure 4a), however,

it can now be all performed in a maximum of 10

minutes (see Figure 4b). The reduction in time to

perform this activity was achieved due to the use

of the wearable, which generates the measurement

of blood pressure and oxygenation automatically,

without the need to prepare the patient with the

devices that were used to control the mentioned

vital signs.

• Transfer the Patient to a Medical Center: we

identiﬁed that this task (see Figure 5) was previ-

ously performed between 21 to 60 minutes (80%

of the time) and more than 60 minutes (20% of

the time) (see Figure 5a), however, it can now be

all performed in a maximum of 20 minutes (see

Figure 5b). The reduction to carry out this activ-

ity was achieved due to the use of telemedicine,

which reduces the need to travel to a medical cen-

ter periodically.

Technological Solution to Optimize the Alzheimer’s Disease Monitoring Process, in Metropolitan Lima, using the Internet of Things

183

(a) Pre-validation (b) Post-validation

Figure 4: Measuring Blood Pressure and Oxygenation (in minutes).

(a) Pre-validation (b) Post-validation

Figure 5: Transferring the Patient to a Medical Center (in minutes).

(a) Pre-validation (b) Post-validation

Figure 6: Supporting Cognitive Stimulation Activities (in minutes).

• Activities to Support Cognitive Stimulation:

we identiﬁed that this task (see Figure 6) was

previously performed between 21 to 60 minutes

(80% of the time) and more than 60 minutes (20%

of the time) (see Figure 6a), however, it can now

be all performed in a maximum of 20 minutes (see

Figure 6b). The reduction to carry out this activ-

ity was achieved by the use of the mobile applica-

tion, which includes games aimed at the follow-

ing cognitive functions: memory, calculation and

concentration. For this reason, the search time for

activities related to cognitive functions decrease,

according to the values indicated above.

• Supporting Physical Exercise Activities: we

identiﬁed that this task (see Figure 7) was previ-

ously performed between 11 to 20 minutes (20%

of the time) and around 21 to 60 minutes (80% of

the time) (see Figure 7a), however, it can now be

all performed in 30 minutes in average (see Fig-

ure 7b). The reduction to perform this activity,

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(a) Pre-validation (b) Post-validation

Figure 7: Supporting Physical Exercise Activities (in minutes).

(a) Pre-validation (b) Post-validation

Figure 8: Communication with an Specialist in case of complication (in minutes).

like the previous activity, was achieved by the use

of the mobile application, which includes recom-

mendations for physical exercises, which is sug-

gested by the patient’s doctor. For this reason, the

search time for physical exercises decreases, ac-

cording to the values indicated above.

• Communication with an Specialist in Case of

Complication: we identiﬁed that this task (see

Figure 8) was previously performed between 11 to

20 minutes (60% of the time) and around 21 to 60

minutes (40% of the time) (see Figure 8a), how-

ever, it can now be all performed in 22 minutes in

average (see Figure 8b). The reduction to perform

this activity, like the last two mentioned before,

was achieved by the use of the mobile applica-

tion, which includes a section on ”Generate alert”

for the doctor, in case the ranges of the variation

of the patient’s vital signs are outside the limits es-

tablished by the doctor and by the information that

the he maintains on the website of the patient’s re-

sults. For this reason, the time taken to explain to

the doctor how the patient’s health behavior has

been in a given period is reduced because the doc-

tor has all the variation generated day by day.

In addition to the aforementioned results, the med-

ical specialists who participated in the survey indi-

cated opportunities for improvement, such as:

• Add alerts for taking medications for the patient

and caregiver.

• Include the wearable activation functionality from

the caregiver interface.

• Include other measurement variables such as: step

count and heart rate.

Likewise, the general opinions that the respon-

dents commented regarding the project presented

were the following:

• The solution not only improves the quality of life

of the patient, but also of the family member and

caregiver.

• Deﬁnitely the doctor’s monitoring is more efﬁ-

cient with the results obtained from the patient.

• It is a good starting point, the solution has a lot of

potential.

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185

5 CONCLUSIONS

Based on the information indicated in the previous

sections, the reduction of transfer costs, emergency

service costs and time associated with monitoring

Alzheimer’s disease has been demonstrated, the op-

timizing the EA monitoring process.

Likely, as a continuation of this work, we plan

to expand the scope of the solution by incorporating

the elderly, without restriction of having Alzheimer’s

disease. This approach is done because of what this

population should have constant monitoring about its

vital signs, to prevent future diseases to which they

may be vulnerable due to their age. Evenmore, store

all the data using blockchain (Cueva-S

anchez et al.,

2020) technology to ensure security and accesibility.

For this reason, adding more variables such as

temperature control, stress management, step count-

ing, will allow you to have accurate control over the

current state of an elderly person, which the doc-

tor will be able to make preventive decisions before

your patient. Taking this platform into cloud (Xu

et al., 2020) could be a interesting topic or with other

kinds of wearables (Roopaei et al., 2018) or using a

chatbot to detect the symptoms (Solis-Quispe et al.,

2021), and therefore extract patterns from the ob-

tained data (Ugarte et al., 2015).

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