ArticlePDF Available

m-SFT: A Novel Mobile Health System to Assess the Elderly Physical Condition mobile health; Healthy aging

Authors:

Abstract and Figures

The development of innovative solutions that allow the aging population to remain 1 healthier and independent longer is essential to alleviate the burden that this increasing segment of the 2 population supposes for the long term sustainability of the public health systems. It has been claimed 3 that promoting physical activity could prevent functional decline. However, given the vulnerability of 4 this population, the activity prescription requires to be tailored to the individual's physical condition. 5 We propose m-SFT (mobile Senior Fitness Test), a novel m-health system, that allows the health 6 practitioner to determine the elderly physical condition by implementing a smartphone-based version 7 of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a 8 comparative study in 7 volunteers (53-61 years) between the original SFT and the proposed m-health 9 system obtaining high agreement (Intra-class correlation coefficient (ICC) between 0.93 and 0.99). The 10 system usability has been evaluated by 34 independent health experts (Mean=36.64 years; Standard 11 Deviation=6.26 years) by means of the System Usability Scale (SUS) obtaining an average SUS score 12 of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health system for 13 the evaluation of the elderly physical condition, also useful in intervention programs to follow-up 14 the patient's evolution. 15
Content may be subject to copyright.
sensors
Article
m-SFT: A Novel Mobile Health System to Assess
the Elderly Physical Condition
Raquel Ureña 1,* , Francisco Chiclana 1,2 , Alvaro Gonzalez-Alvarez 3,
Enrique Herrera-Viedma 2and Jose A. Moral-Munoz 4,5
1
Institute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University,
Leicester, LE1 9BH, UK; chiclana@dmu.ac.uk
2Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of
Granada, 18071 Granada, Spain; viedma@decsai.ugr.es
3Department of Computer Science, University of Cadiz, 11519 Puerto Real, Spain;
alvaro.gonzalezalvarez@alum.uca.es;
4Department of Nursing and Physiotherapy, Universidad de Cádiz, 11009 Cádiz, Spain;
joseantonio.moral@uca.es
5Institute of Research and Innovation in Biomedical Sciences of the Province of Cádiz (INiBICA),
Universidad de Cádiz, 11009 Cádiz, Spain; joseantonio.moral@uca.es
*Correspondence: raquelurenaperez@gmail.com
Received: 1 March 2020; Accepted: 2 March 2020; Published: 6 March 2020


Abstract:
The development of innovative solutions that allow the aging population to remain
healthier and independent longer is essential to alleviate the burden that this increasing segment of the
population supposes for the long term sustainability of the public health systems. It has been claimed
that promoting physical activity could prevent functional decline. However, given the vulnerability of
this population, the activity prescription requires to be tailored to the individual’s physical condition.
We propose mobile Senior Fitness Test (m-SFT), a novel m-health system, that allows the health
practitioner to determine the elderly physical condition by implementing a smartphone-based version
of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a
comparative study in seven volunteers (53–61 years) between the original SFT and the proposed
m-health system obtaining high agreement (intra-class correlation coefficient (ICC) between 0.93 and
0.99). The system usability has been evaluated by 34 independent health experts (mean = 36.64 years;
standard deviation = 6.26 years) by means of the System Usability Scale (SUS) obtaining an average
SUS score of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health
system for the evaluation of the elderly physical condition, also useful in intervention programs to
follow-up the patient’s evolution.
Keywords:
senior fitness test; physical condition; elderly; physical condition assessment; m-health;
mobile health; healthy aging
1. Introduction
The aging of the population is a global phenomenon that presents its highest impact in the
developed countries. In the case of the European Union, the average life expectancy is over the
80 years, an increase of ten years since 1970, with the senior population exceeding 80 years old being
the fastest growing segment. In fact, this segment is expected to represent 20% of the older population
by 2050. Besides, both the aging of the population and the decreasing birth rates are motivating that the
demographic old-age dependency ratio, (the ratio between the people aged 65 or above with respect to
those aged 15–64), to be expected to exponentially increase in the upcoming decades, from about 2% in
2010, it rose to 29.6% in 2016 and is projected to eventually reach 51.2% by 2070. Consequently, in the
Sensors 2020,20, 1462; doi:10.3390/s20051462 www.mdpi.com/journal/sensors
Sensors 2020,20, 1462 2 of 17
case of the EU, it would evolve from four working-age people for every person aged over 65 years in
2010 to around two working-age persons over the projection horizon [1,2].
This change in the age profile implies a population with more physical limitations. This fact
entails an increasing burden for the governments, making long term sustainability of the pubic health
system a key challenge for the countries’ economies [
3
]. For example, when an individual from a
young segment of the population becomes aware of a illness, she receives a diagnosis and usually a
treatment converts the condition into a short-term illness [
4
]. Conversely, in the case of the elderly,
92% have at least one chronic disease, and 77% suffer from at least of two [5].
Therefore, there is an imperative need to develop innovative solutions that enable the aging
generation continue independent longer and to make them responsible of their own health habits and
physical function. That implies a shift, from treatment towards prevention, in the public health care
paradigm with regards to the age-related diseases [6].
With this regard, it has been demonstrated that physical activity, defined in [
7
] as “any bodily
movement produced by skeletal muscles that results in energy expenditure above the basal resting
level”, presents high benefits for the people at both physiological and psychological levels [
8
],
including the elderly. In the case of the elderly, the regular practice of physical activity prevents
functional decline, osteoporosis, frailty, falls, and fractures, decreases the risk of cardiovascular disease,
type 2 diabetes, and certain cancers, and reduce the risk of premature mortality. However, in spite
these undoubted benefits, this segment of the population remains largely inactive. Thus, finding
effective ways to increase and maintain physical activity levels in older people over prolonged periods
constitute a challenge [
9
]. With this particular, technologies that enable ongoing exercise are gaining
importance as the proportion of older people in the population increases making the resources to
provide rehabilitation care to become scarce [
10
]. However, several studies support the fact that in
order to obtain the maximum benefits of the training, the technology prescription and health coaching
support requires to be tailored to the functional and personal characteristics of each individual [
11
].
In consequence, developing tools that asses the physical condition in an easy, and cost-effective way [
8
],
becomes more than necessary.
This is a context where the use of e-health plays a key role. According to [
12
] “E-health is an
emerging field in the intersection of medical informatics, public health and business, referring to
health services and information delivered or enhanced through the Internet and related technologies”.
Furthermore, according to the World Health Organization (WHO), “the strengthening of health systems
through e-Health reinforces fundamental human rights by improving equity, solidarity, quality of
life and quality of care.” A particular application of e-health is the case of m-health, that is, the use
of smartphone applications as tools to deliver medical information and provide care. m-health is
becoming a feasible and helpful utility not only for perioperative patient care but also for patient
evaluation and evolution assessment [13].
In this contribution we leverage the power of m-health to develop a mobile Senior Fitness
Test (m-SFT), a new m-health system to assess the elderly physical condition. m-SFT consists of an
electronic implementation of the Senior Fitness Test (SFT) proposed by Rikli et al. in [
14
]. To do so in a
cost-effective and easy-to-use way, the proposed approach uses the built-in sensors in an inexpensive
android smartphone [
15
] to automatically evaluate, record and control the individual’s muscle strength,
the lower and upper limbs flexibility, the aerobic endurance, and the agility. In that way, this test was
recently implemented in a smartphone-based system to monitor the elderly daily physical activity [
16
].
This contribution is structured as follows: Section 2.1 presents an overview of the m-health apps
focusing specially on the elderly. Section 2.2 describes the fundamental principles of SFT, whereas
Section 3.1 presents the proposed m-health system describing the sensor set up. An evaluation of both
the technical reliability and the usability of the proposed system is shown in Section 3.2. Finally the
discussion and conclusions and the future research challenges that this contribution poses are pointed
out in Sections 4and 5.
Sensors 2020,20, 1462 3 of 17
2. Material and Methods
2.1. Related Work
m-Health has been defined by the WHO as “medical and public health practice supported by
mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs),
and other wireless devices” [
13
]. m-Health includes the utilization of all the smartphone’s core utilities,
ranging from voice and short messaging service to localization system, built in movement sensors
and external wearable devices, i.e., activity monitoring bracelets and smart watches, connected via
bluetooth. From the economical point of view, given the almost omnipresent availability of mobile
technologies, the number of m-health apps is increasing exponentially, for example, in 2016 the global
number of m-health apps reached 259,000, and the rise is expected to continue. So far, US represents the
largest m-health market whereas Asian-Pacific region, Latin America and Europe constitute increasing
markets predicted to grow within the next five years. In fact, by 2022, the global market for m-health
apps is expected to reach 102.43 billion [17]. From the health perspective, m-Health enables non-stop
health monitoring at both individual and population level, and may encourage healthy behaviours
that might reduce or even prevent health problems [
18
]. The actual m-health market can be widely
classified in the following groups:
Chronic care management apps. This category contains the apps designed to deal with Chronic
diseases and their symptom. This category enclosed the apps for managing blood pressure,
glucose levels for diabetes, mental health and other illnesses. Besides the applications in the
public app markets, from the research perspective various interventions have been proposed in the
context of clinical studies, such as depression treatment [
19
], diabetes control [
20
], hypertension
control [21], and psychological support [22,23].
Healthcare and Fitness Apps
. This type of applications seeks for a behavioural change in the
users, by tracking their habits with respect to the meals and the sport and providing tailored
recommendations. They can be classified in two wide groups: (i) Apps that track the calorie
intake such us Lifesum [
24
]. (ii) Apps that register physical activity using not only the sensors in
the phone but other weareable devices such us activity bracelets or smartch watchs. Example of
this apps are GoogleFit [
25
] or Endomondo [
26
] among others. Among the clinical studies we
can remark the one in [
27
] that aims for a reduction of calorie intake by use of personal digital
assistant applications for diet and exercise, or the one in [
28
] which consist on mobile phone
application intervention to increase physical activity levels.
Medication Management Apps
. Within this group they are comprised the apps that keep track
of medication intake in order to improve its adherence among patients, they are specially useful
among the elderly.
Personal Health Record Apps
. Among this category we can find the applications that allow
patients to store their medical conditions data, allergies etc. and share it with their doctors.
An increasing sector of the market consists of apps designed for the elderly that can be found
in the main app catalogs, Google Play and Apple store [
6
]. Most of these apps are designed for this
following two main purposes:
Emergency situations detection
. This sector involve the apps that are able to detect if the elderly
is in a danger situation, i.e., falls or disorientation, if this is the case, the app fire a flag to the
elderly’s caregivers or relative or to the emergency services to assist the person in danger.
Behaviour changing and physical tele-rehabilitation
. Among this category we can find out
as well some e-health platforms that provides advice and training exercises on how to recover
from certain problems. For example activehip [
29
] is a tele-rehabilitation platform designed for
elderly patients recovering from a hip fracture. DIGIREHAB, [
30
] that is a Danish platform that
provides objective assessment of each patients’ need for assistance and his/her general level
Sensors 2020,20, 1462 4 of 17
of ability. Together, these assessments form a precise image of the patients’ physical potential
for rehabilitation. We can find out as well other prototypes of mobile solutions for the elderly
population that suffers from low vision based on a digital image enhancement [23,3134].
In the field of the test applications we can point out various solutions that requires of different
sensors or smartphones fixed to the body (e.g., the lower back), for example the instrumented versions
of the commonly-used clinical test Timed Up-and-Go [
35
] or a digital version of the trust endurance
presented in [
36
]. Furthermore, a web based implementation of the aforementioned Senior Fitness
Test [
14
]. that can be used for entering and analyzing the SFT test scores, creating individual or
aggregated reports, and generating program outcome statistics has been proposed in [37].
In the light of the existing m-health apps we can conclude that the majority of m-health
applications for the elderly are done either with reminding purposes either with advice purposes,
but there is no application that carries out the assessment of the physical condition of the elderly in an
automatic way using uniquely an inexpensive android mobile phone.
2.2. Senior Fitness Test
The Senior Fitness Test consists of a battery of test items that covers up the major components of
fitness for older adults. In concrete, it evaluates the physical attributes that are required to perform
daily activities in later life in terms of strength, endurance, flexibility, agility, and balance [
14
]. The main
advantages of SFT are based on the facts that it is easy to understand, quick to administer, and safe.
Furthermore, in comparison with other tests, it requires a lower number of tools to be performed.
The SFT is composed of eight test item in order to asses the muscle strength, the lower and upper
limb flexibility, the aerobic endurance, and the agility. In Table 1, the different test items that composed
the SFT are described in detail; furthermore, the values reported for Rikli and Jones’s [
14
] sample
are stated.
Table 1. Senior Fitness Test (SFT) tests description.
Test Measure Description 60–69
Years
70–79
Year
80–89
Years
Chair Stand
Test (n)
Strength of
lower limbs
The number of times that person is able to stand
up and sit without using the arms during a lapse
of time of 30 s
14.0
(2.4)
12.9
(3.0)
11.9
(3.6)
Arm Curl
Test (n)
Strength of
upper limbs
The number of times that a person is able to fold
the arm between 90 to 0 degrees holding a lift of
5 lb (2.27 kg) for women and 8 lb (3.63 kg) for men
during a lapse of time of 30 s
19.8
(4.1)
18.2
(3.9)
16.5
(4.1)
2-min Step in
Place Test (n)
Aerobic
endurance
the number of times that starting in a stand up
position, a person can raise the knees to a height
halfway between the iliac crest and middle of the
patella during the lapse of time of two minutes.
100.4
(9.0)
92.6
(16.0)
83.5
(22.6)
Chair Sit and
Reach Test
(cm)
Flexibility
of the lower
body
This test item asses the distance that a person can
reach the toe (minus score) or beyond the toe (plus
score) with fingers. The starting position is seated
on the edge of a chair, with a leg extended straight
in front of the hip with heel on floor flexed at 90.
1 (14)
1 (15)
8 (15)
Back Scratch
Test (cm)
Flexibility
of the upper
limbs
Distance between (or the overlap of) the middle
fingers behind the back when trying to touch the
middle fingers of both hands together behind the
back (measure to the nearest 1/2 inch).
3.0
(5.0)
1 (8)
5 (11)
8-Feet (2.45
m) Up and
Go Test (s)
Agility and
dynamic
balance
The lapse of time a person takes to stand up from
a chair, walk 8 feet (2.45 m) to and around a cone,
and return to the chair (perform twice and measure
time to the nearest 1/10th of a second, recording
fastest time).
5.2
(0.6)
6.1
(1.2)
7.1
(2.0)
Sensors 2020,20, 1462 5 of 17
3. Results
3.1. The Proposed App, m-SFT
This contribution proposes a new m-health platform to automatically asses the elderly physical
condition by means of the built in sensors in an Android phone. The choice of this Operating System
to develop our proposal is based in both its bigger implantation in the market (android smart-phones
constitute the 92% of the mobile phones in the Spanish market) and the availability of low cost devices.
In the following the technical details on how this app can be implemented are explained.
3.1.1. App Implementation
The architecture of the proposed system is composed of four interconnected layers, as depicted in
Figure 1, namely the data storage manager, the data processing manager, visualization manager and
the user interface, UI.
Figure 1. m-SFT architecture.
The data storage manager implements a local SQLite database [
38
] stored on the mobile phone
built in memory. This way, the fully off line working capability is ensured. This database keeps one
register for each patient storing his/her ID, age, gender, contact information, and the test results.
The entity-relationship diagram for the database is depicted in Figure 2. The data base is composed
of fourtables namely Persona, Session, Test, and Results. The table Persona stores the user profile
information. The table Session includes the information related with each of the test sessions that
a given user accomplishes. The table Test contains the type of test and its description and finally
the table Result stores the results of each test item for each session for each user. Moreover the
information coming from the sensors during the execution of the test, detailed in the following
subsection, is buffered and periodically stored in the sensors’ table, in order to ensure efficiency and to
Sensors 2020,20, 1462 6 of 17
facilitate the computation. Once the test is completed and the results are calculated, these results are
updated in the corresponding patient table.
The visualization manager provides graphical representation of the historical test results that
are depicted in the UI, as it is shown in Figure 9. This manager is developed on top of the
MPAndroidChart [39], an open source library for statistical graphics.
Persona
person_id
sex
date of Birth
weight
height
photo
Results
result_id
date
Session
session_id
active
date
Test
test_id
name
description
has
has
gets
holds
does
1
*
* *
1
1
*
*
*
*
Figure 2. Diagram–entity relationship for the database.
The data processing manager is in charge of the interface with the sensors, estimating the angles
and the repetition for each one of the test elements. In the following subsection we detail how this
interface has been implemented. Furthermore in subsection 3.1.3 we discuss the app UI.
3.1.2. Sensor Interface
The Android platform provides a range of built in sensors that allow to monitor the device motion.
These sensor can be broadly classified into hardware-based and software-based and their availability
varies depending on the device model. Among the hardware-based sensor we can find both the
accelerometer and the gyroscope, whereas the gravity, linear acceleration, rotation vector, significant
motion, step counter, and step detector sensors could be either hardware-based or software-based
depending on the device. The majority of the devices always include the accelerometer and some
of them the gyroscope. The availability of the software-based sensors depends on the built in
hardware-based ones, being the last ones the data source for the former [
40
]. In the case of m-SFT,
in order to recognize the movement and the repetitions for the different test items, the proposed
system uses the gravity sensor. This is a software-based sensor that asses the effect of the earth gravity
acceleration combining both the output from the accelerometer and the gyroscope to remove the linear
acceleration. This sensor ’s output consists on a three dimensional vector indicating the direction and
magnitude of the gravity in m/s
2
in the direction of the three spatial axis, x,yand z[
40
]. The gravity
sensor is available in each Android device with Android 2.3 (API nivel 9). The release of this version
of Android date from December 2010. Therefore the majority of the Android Devices manufactured
after 2010 includes the gravity sensor [
41
]. Therefore, the reliability of m-sft is guaranteed for all the
devices that has Android Version 2.3 or older.
In the following how the sensor set up is leveraged to monitor each one of the items that composed
the Senior Fitness test is detailed.
Sensors 2020,20, 1462 7 of 17
Chair Stand test
To carry out this test item, the mobile device has to be attached to the leg as
depicted in Figure 3a. At the beginning of each repetition the acceleration in the direction of
the
y
-axis is about 0 m/s
2
, that is, the user is sat and the phone is almost parallel to the floor.
Then the user has to stand up placing the phone upside down and perpendicular to the floor,
being the acceleration in the
y
axis about
9.8 m/s
2
. Finally the repetition is completed once the
acceleration in
y
becomes 0 again. Figure 4shows the values of the acceleration for this exercise
when the user is standing and when is sit, while Figure 5shows the evolution of the acceleration
in the
y
-axis during several iterations. Note that the changes of the acceleration in the
z
-axis is not
relevant in this case. This change is due to slightly changes in the phone height when attached to
the leg and the acceleration in the
x
axis is not relevant and normally is close to zero. The datum
point in the acceleration for the classification is the change in the value of the acceleration in the
y
-axis when it reaches
9.8 m/s
2
indicating that the user is completely vertical. Finally, it is worth
remarking that when designing our system, we considered more complex machine learning based
classifiers. Nevertheless, these type of classifiers are not suitable to provide real time operation
in a low resource smartphone. Therefore, giving that the performance of the proposed detection
algorithm is good enough in practice, we decided to keep it to ensure the reliability of the system
and the real time performance even with a low cost smartphone. However the inclusion of more
sophisticated movement detection algorithms for powerful smartphones will be considered as
future work.
(a) (b)
Figure 3. Positions of the mobile phone for the test: (a) Leg; (b) Arm.
Sensors 2020,20, 1462 8 of 17
(a) (b)
Figure 4. Gravity for the legs strength test: (a) User sit; (b) User standing.
Figure 5. Acceleration in the Y-axis direction during the legs strength test.
Arm curl test
In this case the user has the arm fully extended with the fist towards down, and with
the mobile device attached to the forearm, leaving it upside down as in Figure 3b. In this posture,
the acceleration in
y
is negative. At the mid point of the exercise the acceleration on
y
becomes
positive and higher to 7.5 m/s
2
, finally, the repetition is considered as completed when the
acceleration comes back to the initial state. In Figure 6a,b we can observe both the acceleration at
the beginning and at the midpoint of the exercise respectively.
Sensors 2020,20, 1462 9 of 17
(a) (b)
Figure 6. Gravity for the arm strength test: (a) Initial; (b) Final.
2-min step in place test
To accomplish this test item the phone should be attached in the same
way than for the chair stand test, (see Figure 3a). At the beginning the user is standing, and so
the phone is upside down and perpendicular to the floor, being the acceleration in
y
around
9.8 m/s
2
. At the mid point of the exercise the acceleration in
y
evolves to 0 m/s
2
( the user will
be sit and so the phone will be almost parallel to the floor). The repetition is complete once the
acceleration comes back to the initial state.
Chair sit and reach test and back scratch test
For this test item we have not implemented yet the
feature that allows assessing the distance between the two hands, therefore in this first version
of the platform this distance needs to be measured and introduce manually in the system by the
practitioner. For future implementation, the option that we are considering is the inclusion of
image analysis techniques to determine this distance in both tests [
42
] from a calibrated image
taken during the test execution. We are aware that this is a valid option when the system is used
by a health professional to monitor and track patients, but is difficult to use by an end-user, in that
second case probably additional sensors will be required.
8-foot up and go test
This test item does not requires the patient to attach the phone, since the
only sensor required is the chronometer. Therefore either the patient or the health practitioner
has to start and stop the chronometer in the app, and the completion time will be automatically
updated in the patient records.
3.1.3. User Interface
In the following a detailed explanation of each of the views that compose the m-SFT’s UI is given.
The navigation flow between them is presented in Figure 7and these views are depicted in Figures 8
and 9.
When the app is launched, the practitioner is directed to a view with a list of all the registered
patients and the option of including a new one. To add a new patient to the system, the practitioner
should provide the following patient’s information: ID, birth date, sex and, if desired, a picture of the
patient. Alternatively, by selecting one of the patients in the main list, the practitioner would access
the patient’s record that include the number of test sessions accomplished, the results and statistics,
and the uncompleted sessions. Once the practitioner has chosen a patient, he/she can start a new test
Sensors 2020,20, 1462 10 of 17
session. To do so the practitioner will be directed the test menu view, where the test item to do has to
be selected. If the session has been resumed it will appear the completed test item among with the
patient’s performance. Once the practitioner selects a test item, a view with the instructions appears,
including pictures explaining how to attach the device and how to perform the test. After pressing
the button continue the view of the specific test item is displayed showing its duration in seconds of
the test (this parameter can be adjusted by the practitioner), the picture with the instructions for the
test and the number of repetitions if required. Once the phone is properly attached to the patient and
he/she is ready to go, the practitioner can press start and the device will start registering the number of
repetitions and the time. Once the time is over they device rings and stops registering the repetitions.
To asses the overall patient’s performance in the whole Senior Fitness Test the system will evaluate
whether the results are between the normal interval depending on the sex and the age of the patient
according to the threshold pointed out in [
14
]. Notice that all the texts in the application are in Spanish,
since the target practitioners and patients for the study were Spaniards. However the application can
be easily translated to English.
Figure 7. Mobile Senior Fitness Test (m-SFT) views flow.
Sensors 2020,20, 1462 11 of 17
Figure 8. Tests views.
Figure 9. Results views.
3.2. m-SFT Evaluation
As mentioned the proposed system has been designed as a cost-effective and easy-to-use tool
for a health practitioner to record and asses the physical condition of older adults. In this section,
we present an preliminary analysis of the technical reliability and usability of the proposed system.
In order to carry out the technical reliability evaluation, seven volunteers, four females and
three males ranging from 53 to 61 years old were assessed using the traditional test protocol
described in Section 2.2 and using the m-SFT tool. In this first stage, we selected this population
Sensors 2020,20, 1462 12 of 17
to obtain an approximation to the tool technical reliability. A higher sample with an adequate
range of years will be needed to obtain solid conclusions; a future evaluation about the patient’s
condition classification capacity will be performed. An experienced physiotherapist performed this
evaluation. Before performing the assessment, the volunteers were informed of the research aims, risks,
and benefits of participation. Next, they read and signed an informed consent form. The participants
dressed comfortably and they were guided through the different tests. Participants were evaluated the
same day at two different times, with a resting period of one hour [
36
]. In order to minimize the effect
of measuring the same person in two different times, the assessment method was randomized using
the coin toss method. The results obtained for each participant in both test modalities are shown in
Table 2.
Before performing the statistical analysis, the results obtained were translated to a spreadsheet.
Concerning these data, an analysis of the inter-rater reliability was performed (Table 3) using the
SPSS version 24.0 for Mac (IBM Corporation, Armonk, NY). The statistical analysis of the intraclass
correlation coefficient (ICC) (
ρ
), Cronbach’s
α
estimator, and Bland-Altman plots were carried out and
the agreement degree between the two assessment approaches is shown in Table 3. In order to interpret
the results please note that an ICC (
ρ
) below 0.4 indicates poor inter-rater reliability; between 0.4 to
0.75 means fair to good reliability; and over 0.75 the reliability can bee considered as excellent [
43
,
44
].
In the case of the Cronbach’s
α
results below 0.5 were considered unacceptable; between 0.5 to 0.9 the
acceptability ranges from poor to good and the values above 0.9 were considered as excellent [45].
Furthermore, the graphical representation of the agreement between two techniques, known
as Bland-Altman plots [
46
], was used to help understand the measurements of the two procedures
against their averages. In that way, we plotted the results of those tests based on the smartphone
sensors (Figure 10).
In views of these results, we can state that the proposed system has high technical reliability.
All the measured variables provided high statistic values, ICC (
ρ
) and Cronbach’s
α
above 0.9. Therefore
m-SFT can be considered as a reliable tool to assess the physical condition of older people.
Table 2. Case study results.
Patient ID 1 2 3 4 5 6 7 8
Gender Male Female Female Male Female Female Male
Age 54 53 60 61 59 57 61
Chair Stand Test (SFT) 14 15 12 14 10 16 12
Chair Stand Test (m-SFT) 12 13 12 13 8 13 9
Arm Curl Test (SFT) 23 16 15 20 14 16 15
Arm Curl Test (m-SFT) 22 15 15 20 14 15 13
2-min Step in Place Test (SFT) 75 80 93 97 72 74 85
2-min Step in Place Test (m-SFT) 69 76 83 87 66 67 78
Chair Sit and Reach Test (SFT) 1 3 5 3 2.5 2 2
Chair Sit and Reach Test (m-SFT) 1 3.5 5 2.5 3 2 2
Back Scratch Test (SFT) 3.5 1 2 2.5 2.5 14
Back Scratch Test (m-SFT) 3 1.5 2.5 2 2.5 13.5
8-Foot Up and Go Test (SFT) 5.3 6.2 3.5 4.1 6.1 3.3 6
8-Foot Up and Go Test (m-SFT) 5 6.4 3.6 4.5 5.4 3.5 6.2
Sensors 2020,20, 1462 13 of 17
Table 3. Inter-rater reliability between traditional Senior Fitness Test and m-SFT.
Variable ICC (ρ)CI 95% of ICC+Cronbach’s α
Chair Stand Test 0.93 0.58–0.99 0.93
Arm Curl Test 0.99 0.86–0.99 0.99
2-min Step in Place Test 0.98 0.83–0.99 0.98
Chair Sit and Reach Test 0.99 0.98–0.99 0.99
Back Scratch Test 0.99 0.99–1 0.99
8-Foot Up and Go Test 0.97 0.85–0.99 0.97
ICC (ρ) was calculated using a one-way random model.
+ICC indicates the intra-class correlation coefficient. CI, confidence interval.
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Chair Stand Test
(a)
-1
-0.5
0
0.5
1
1.5
2
2.5
Arm Curl Test
(b)
0
2
4
6
8
10
12
14
2-min Step in Place Test
(c)
Figure 10.
Agreement analysis between SFT and m-SFT assessment through Bland-Altman plots:
(
a
) Chair Stand Test, (
b
) Arm Curl Test, and (
c
) 2-min Step in Place Test. The mean of differences (
¯
x
) is
represented by a black line, while the limits of agreement ( ¯
x±1.96σX) are depicted in red.
Another important concern when developing an app for an specific segment of the population
refers to its usability. Formally the usability can be considered as the facility of use of a tool or device
giving to the user-device interaction a central role in the evaluation [
6
]. The usability of the proposed
m-health app has been evaluated by employing the System Usability Scale (SUS) [
47
,
48
]. This scale,
widely used in the industry, measures the user experience concerning various sort of technologies.
The SUS consists on a ten items questionnaire answered by the tester using a five-point scale ranging
from “strongly disagree” to “strongly agree”. The result is an estimated percentage of usability known
as SUS score. Scores below 50% are considered as unacceptable whereas over 70% are considered as a
good acceptability [
49
]. In our case, to carry out the usability evaluation, a total of 19 physiotherapists
and 15 medical doctors (mean = 36.64 years; SD = 6.26 years; 18 males and 16 females) were asked
to use the app during a small trial. First, a brief training on how to use the app were given to the
experts, then, they were requested to carry out at least one whole SFT before answering to the SUS
questionnaire. The average SUS score obtained is 84.4%, as shown in Figure 11, indicating high levels
of acceptability, facility to use and confidence.
Sensors 2020,20, 1462 14 of 17
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35
SUS Scores
Participants
Mean: 84.41
Figure 11.
System Usability Scale (SUS) scores obtained from thirty four experts after using the
proposed application.
4. Discussion
In this contribution a new m-health system is described and evaluated, hypothesizing that the
use of this type of technologies is useful for the clinical practice. In that way, the preliminary results
suggest that a high agreement between the app and the traditional approach exists. Thus, it supposes
a promising system to perform the analysis of the elderly physical condition, and also the follow-up
of a specific intervention in an easy-to-use manner. Nonetheless, in the following paragraphs, some
comments about the results need to be stated.
With regard to the technical reliability analysis the number of participants is not elevated, therefore
establishing a strict conclusion based on this population is difficult. However, the ICC (
ρ
) and
Cronbach’s
α
values are similar to previous validation studies. For example, the Knee Goniometer
App [
50
], iHandy level app [
51
], or the timed-up-and-go test [
52
] also uses the inertial sensors and
obtained ICC (
ρ
) and Cronbach’s
α
values above 0.90. Moreover, the analysis was performed in a
laboratory controlled environment, and so some other issues may arise in a non-controlled clinical
environment. With this regard, we should point out that the system has been tested only with
healthy older adults able to perform bio-mechanically correct movements. However, in a real
clinical environment, there might be some participants not able to execute a perfect movement,
and consequently, in this case, the system may present some inaccuracies. These issues will be
addressed in future versions of the platform.
With regard to the usability assessment, given that the SUS score obtained is over 80% m-SFT can
be considered as an useful tool for the clinical environment. However, according to the experts’ opinion,
although the current version and design are adequate to a laboratory test, it would be useful to add a
more commercial-like appearance to the system in a future version. In that way, there are no usability
analyses about an app employing SFT, but similar results were found in other apps. For example,
TouchStream, an app to assess older adults with cancer, obtained a mean SUS of 78.7% [
53
]. Another
app, assessing the fall risk in older adults obtained an average from 79 to 84 [54].
Given these promising results, some limitations have to be addressed. Formal validation of
the m-SFT needs to be carried out, involving a larger number of senior participants and taking
into consideration both laboratory and clinical conditions to assess the differences between them.
The results obtained from the seven participants cannot be extrapolated to the general older adults
population. Furthermore, although the tests present good reliability, they can change depending on
the criteria for classification.
5. Conclusions
In this contribution we present a new intelligent m-health tool that asses the elderly physical
condition by means of an electronic implementation of the well-known SFT. The proposed app is
able to automatically evaluate the elderly physical condition by only using the built in sensors in
Sensors 2020,20, 1462 15 of 17
an inexpensive Android Phone. The technical reliability of m-SFT has been tested by carrying out
a comparative study between the original SFT and the proposed m-health system obtaining high
agreement between both approaches (ICC between 0.93 and 0.99). Furthermore the system usability has
been evaluated by independent health experts obtaining an average SUS score of 84.4, indicating high
levels of acceptability, facility to use and confidence. Both results show that m-SFT is a reliable and
easy-to-use m-health system for the evaluation of the elderly physical condition that also may be
useful in intervention programs in order to electronically asses and record the patient’s evolution.
Author Contributions:
R.U. and J.A.M.M. are the principal researchers of this study and main authors of this
work. A.G.A. has implemented the m-SFT application. R.U. and J.A.M.M. have written the paper. F.C. and E.H.V.
reviewed the manuscript for scientific content. All authors read and approved the final manuscript.
Funding:
This research was funded by the EU Marie Curie grant number
H2020-MSCA-IF-2016-DeciTrustNET-746398 and the National Spanish project grant number TIN2016-75850-P.
The APC was funded by EU Marie Curie grant number H2020-MSCA-IF-2016-DeciTrustNET-746398
Acknowledgments:
The authors would like to acknowledge the financial support from the EU Marie Curie
project H2020-MSCA-IF-2016-DeciTrustNET-746398 and the National Spanish project TIN2016-75850-P.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
References
1.
OECD. OECD Health at a Glance 2013: OECD Indicators. 2013. Available online: http://www.oecd.org/
els/healthsystems/Health-at-a-Glance-2013.pdf (accessed on 21 November 2019).
2.
EU-Statistics. Eurostat People in the EU-Statistics on an Ageing Society; Technical Report; Statistical Office of
the European Communities (EUROSTAT): Luxembourg, 2016.
3.
Oshima Lee, E.; Emanuel, E.J. Shared Decision Making to Improve Care and Reduce Costs. New Engl. J. Med.
2013,368, 6–8.
4.
Merrell, R.C.; Author, C. Geriatric Telemedicine: Background and Evidence for Telemedicine as a Way to
Address the Challenges of Geriatrics. Heal. Inf. Res 2015,21, 223–229, doi:10.4258/hir.2015.21.4.223.
5. National Council on Aging. Healthy Aging Facts; National Council on Aging: Arlington,VA, USA, 2014.
6.
Helbostad, J.L.; Vereijken, B.; Becker, C.; Todd, C.; Taraldsen, K.; Pijnappels, M.; Aminian, K.; Mellone, S.
Mobile Health Applications to Promote Active and Healthy Ageing. Sensors 2017,17, 622.
7.
Caspersen, C.J.; Powell, K.E.; Christenson, G.M. Physical activity, exercise, and physical fitness: Definitions
and distinctions for health-related research. Public Health Rep. 1985,100, 126–131.
8.
College, A.; position stand, S.M. Exercise and Physical Activity in older adults. Med. Sci. Sport Exerc.
1998
,
30, 992–1008.
9.
Iliffe, S.; Kendrick, D.; Morris, R.; Griffin, M.; Haworth, D.; Carpenter, H. Promoting physical activity in
older people in general practice: ProAct65 cluster randomised controlled trial. Br. J. Gen. Pract.
2015
,
65, e731–e738.
10.
Peek, S.T.; Wouters, E.J.; van Hoof, J.; Luijkx, K.G.; Boeije, H.R.; Vrijhoef, H.J. Factors influencing
acceptance of technology for aging in place: A systematic review. Int. J. Med. Inform.
2014
,83, 235–248,
doi:10.1016/j.ijmedinf.2014.01.004.
11.
Hassett, L.; van den Berg, M.; Lindley, R.I.; Crotty, M.; McCluskey, A.; van der Ploeg, H.P.; Smith, S.T.;
Schurr, K.; Killington, M.; Bongers, B.; et al. Effect of affordable technology on physical activity levels and
mobility outcomes in rehabilitation: A protocol for the Activity and MObility UsiNg Technology (AMOUNT)
rehabilitation trial. BMJ Open 2016,6, e012074.
12. Eysenbach, G. What is e-health? J. Med. Internet Res. 2001,3, e20, doi:10.2196/jmir.3.2.e20.
13.
for eHealth, W.G.O. mHealth: New Horizons for Health through Mobile Technologies: Second Global
Survey on eHealth, 2011. Available online: http://www.who.int/goe/publications/goe_mhealth_web.pdf
(accessed on 22 May 2019).
14. Rikli, R.E.; Jones, C.J. Senior Fitness Test Manual; Human Kinetics: New York, NY, USA, 2013; p. 176.
15.
Ignatov, A. Real-time human activity recognition from accelerometer data using Convolutional Neural
Networks. Appl. Soft Comput. 2018,62, 915 – 922, doi:10.1016/j.asoc.2017.09.027.
Sensors 2020,20, 1462 16 of 17
16.
Joosen, P.; Piette, D.; Buekers, J.; Taelman, J.; Berckmans, D.; De Boever, P. A smartphone-based solution to
monitor daily physical activity in a care home. J. Telemed. Telecare 2019,25, 611–622.
17.
Research, Z.M. mHealth Market by Devices, by Stakeholder, by Service, by Therapeutics and by Applications:
Global Industry Perspective, Comprehensive Analysis and Forecast, 2014–2022. Available online: https:
//www.zionmarketresearch.com/report/mhealth-market (accessed on 24 April 2018).
18.
M.N.K., B.; Wheeler, S.; C., T.; Jones, R. How Smartphones are Changing the Face of Mobile and Participatory
Healthcare: An Overview, with Example from eCAALYX. Biomed. Eng. 2011,10, 24.
19.
Bexelius, C.; Löf, M.; Sandin, S.; Trolle Lagerros, Y.; Forsum, E.; Litton, J.E. Measures of Physical Activity
Using Cell Phones: Validation Using Criterion Methods. J. Med. Internet Res. 2010,12, e2.
20.
Desveaux, L.; Shaw, J.; Saragosa, M.; Soobiah, C.; Marani, H.; Hensel, J.; Agarwal, P.; Onabajo, N.; Bhatia, S.R.;
Jeffs, L. A Mobile App to Improve Self-Management of Individuals With Type 2 Diabetes: Qualitative Realist
Evaluation. J. Med. Internet Res. 2018,20, e81.
21.
Carrasco, M.; Salvador, C.; Sagredo, P.; Marquez-Montes, J.; Gonzalez deMingo, M.; Fragua, J.; Rodriguez, M.;
Garcia-Olmos, L.; Garcia-Lopez, F.; Carrero, A. Impact of Patient-General Practitioner Short-Messages-Based
Interaction on The Control of Hypertension in a Follow-up Service for Low-to-Medium Risk Hypertensive
Patients: A Randomized Controlled Trial. IEEE Trans. Inf. Technol. Biomed. 2008,12, 780–791.
22.
Grassi, A.; Gaggioli, A.; Riva, G. The Use of Mobile Narratives for Reducing Stress in Commuters.
Cyberpsychol. Behav. 2009,12, 155–161.
23. Chacon-Lopez, H.; Pelayo, F.J.; Lopez-Justicia, M.D.; Morillas, C.A.; Urena, R.; Chacon-Medina, A.; Pino, B.
Visual training and emotional state of people with retinitis pigmentosa. J. Rehabil. Res. Dev.
2013
,50, 1157–68.
24. LifeSum. LifeSum, 2018. Available online: https://lifesum.com/ (accessed on 27 August 2018).
25. Google. GoogleFit, 2018. Available online: https://www.google.com/fit/ (accessed on 27 August 2018).
26.
Endomondo. Endomondo Sport Tracker. 2018. Available online: https://www.endomondo.com/ (accessed
on 27 August 2018).
27.
Burke, L.E.; Conroy, M.B.; Sereika, S.M.; Elci, O.U.; Styn, M.A.; Acharya, S.D.; Sevick, M.A.; Ewing, L.J.;
Glanz, K. The Effect of Electronic Self-Monitoring on Weight Loss and Dietary Intake: A Randomized
Behavioral Weight Loss Trial. Obesity 2011,19, 338–344.
28.
King, A.C.; Ahn, D.K.; Oliveira, B.M.; Atienza, A.A.; Castro, C.M.; Gardner, C.D. Promoting Physical Activity
through Hand-Held Computer Technology. Am. J. Prev. Med. 2008,34, 138–142.
29.
Active Hip, Plataforma de telerehabilitacion de cadera. Available online: http://activehip.es/ (accessed on
24 August 2018).
30.
DigiRehab. DigiRehab. Available online: https://portal.digirehab.dk/ContentId/8214/Default.aspx?
Language=2057 (accessed on 27 August 2018).
31.
Ureña, R.; Martínez-Cañada, P.; Gómez-López, J.M.; Morillas, C.A.; Pelayo, F.J. A Portable Low Vision Aid
based on GPU. PECCS 2011. In Proceedings of the 1st International Conference on Pervasive and Embedded
Computing and Communication Systems, Vilamoura, Algarve, Portugal, 5–7 March 2011; pp. 201–206.
32.
Ureña, R.; Martínez-Cañada, P.; Gómez-López, J.M.; Morillas, C.A.; Pelayo, F.J. Real-time tone mapping on
GPU and FPGA. EURASIP J. Image Video Process. 2012,2012, 1, doi:10.1186/1687-5281-2012-1.
33.
Martínez-Cañada, P.; Morillas, C.A.; Ureña, R.; López, F.M.G.; Pelayo, F.J. Embedded system for contrast
enhancement in low-vision. J. Syst. Archit. - Embed. Syst. Des. 2013,59, 30–38.
34.
Ureña, R.; Morillas, C.A.; Pelayo, F.J. Real-time bio-inspired contrast enhancement on GPU. Neurocomputing
2013,121, 40–52.
35.
Mellone, S.; Tacconi, C.; Chiari, L. Validity of a Smartphone-Based Instrumented Timed Up and Go.
Gait Posture 2012,36, 163–165.
36.
Banos, O.; Moral-Munoz, J.A.; Diaz-Reyes, I.; Arroyo-Morales, M.; Damas, M.; Herrera-Viedma, E.;
Hong, C.S.; Lee, S.; Pomares, H.; Rojas, I.; et al. MDurance: A novel mobile health system to support trunk
endurance assessment. Sensors 2015,15, 13159–13183, doi:10.3390/s150613159.
37.
Senior Fitness Test 2.0. Available online: https://sft.humankinetics.com/login?ReturnUrl=%2f (accessed on
24 August 2018).
38.
Android Developers, G. Save Data Using SQLite. Available online: https://developer.android.com/
training/data-storage/sqlite.html (accessed on 24 August 2018).
39.
Jay, P. MP Android Chart. Available online: https://github.com/PhilJay/MPAndroidChart (accessed on 24
August 2018).
Sensors 2020,20, 1462 17 of 17
40.
Android Developers, G. Motion Sensors. Available online: https://developer.android.com/guide/topics/
sensors/sensors_motion.html (accessed on 24 August 2018).
41.
Android Sensors Overview. Available online: https://developer.android.com/guide/topics/sensors/
sensors_overview (accessed on 4 February 2020).
42.
White, P.J.; Podaima, B.W.; Friesen, M.R. Algorithms for smartphone and tablet image analysis for healthcare
applications. IEEE Access 2014,2, 831–840.
43.
Palacín-Marín, F.; Esteban-Moreno, B.; Olea, N.; Herrera-Viedma, E.; Arroyo-Morales, M. Agreement
between telerehabilitation and face-to-face clinical outcome assessments for low back pain in primary care.
Spine 2013,38, 947–952.
44.
Moral-Muñoz, J.A.; Esteban-Moreno, B.; Arroyo-Morales, M.; Cobo, M.J.; Herrera-Viedma, E. Agreement
Between Face-to-Face and Free Software Video Analysis for Assessing Hamstring Flexibility in Adolescents.
J. Strength Cond. Res. 2015,29, 2661–2665, doi:10.1519/JSC.0000000000000896.
45. Gliem, J.A.; Gliem, R.R. Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for
Likert-type scales. In Proceedings of the Midwest Research-to-Practice Conference in Adult, Continuing,
and Community Education, DeKalb, IL, USA, 23–28 September 2003 .
46.
Myles, P.S.; Cui, J. Using the Bland—Altman method to measure agreement with repeated measures.
Br. J. Anaesth. 2007,99, 309–311.
47. Brooke, J. SUS-A quick and dirty usability scale. Usability Eval. Ind. 1996,189, 4–7.
48.
Lewis, J.R.; Sauro, J. The factor structure of the system usability scale. In Human Centered Design; Springer:
Berlin/Heidelberg, Germany, 2009; pp. 94–103.
49.
Bangor, A.; Kortum, P.T.; Miller, J.T. An empirical evaluation of the system usability scale. Intl. J. Hum.-
Interact. 2008,24, 574–594.
50.
Milanese, S.; Gordon, S.; Buettner, P.; Flavell, C.; Ruston, S.; Coe, D.; O’Sullivan, W.; McCormack, S. Reliability
and concurrent validity of knee angle measurement: Smart phone app versus universal goniometer used by
experienced and novice clinicians. Man. Ther. 2014,19, 569–574.
51.
Vohralik, S.L.; Bowen, A.R.; Burns, J.; Hiller, C.E.; Nightingale, E.J. Reliability and validity of a smartphone
app to measure joint range. Am. J. Phys. Med. Rehabil. 2015,94, 325–330.
52.
Galán-Mercant, A.; Barón-López, F.J.; Labajos-Manzanares, M.T.; Cuesta-Vargas, A.I. Reliability and
criterion-related validity with a smartphone used in timed-up-and-go test. Biomed. Eng. Online
2014
,13, 156.
53.
Loh, K.P.; Ramsdale, E.; Culakova, E.; Mendler, J.H.; Liesveld, J.L.; O’Dwyer, K.M.; McHugh, C.; Gilles, M.;
Lloyd, T.; Goodman, M.; et al. Novel mHealth app to deliver geriatric assessment-driven interventions for
older adults with cancer: Pilot feasibility and usability study. JMIR Cancer 2018,4, e10296.
54.
Hsieh, K.L.; Fanning, J.T.; Rogers, W.A.; Wood, T.A.; Sosnoff, J.J. A fall risk mhealth app for older adults:
Development and usability study. JMIR Aging 2018,1, e11569.
c
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... In [24], the Arm Curl Test, and Chair Sit and Reach tests were implemented with the older adults to detect their physical condition. ...
Conference Paper
Full-text available
The functional tests are essential to test the functionality of different types of people, and specialty for older adults. The primary purpose of this paper is to create a method for the automatic measurement of the results of the different functional tests. These are the Heel-rise Test, Functional Reach Test, Timed Up and Go Test, Ten Meter Walk Test, Eight hop test, Up-down hop test, Side hop test, Single hop test, Chair Stand Test, Arm Curl Test, and Chair Sit and Reach test. The use of sensors may increase the accuracy of the measurements of these tests. These tests may identify several diseases, and it will be subject to further research in the future.
... In recent years, mobile devices' evolution increased the hardware properties in terms of sensors, computing power, battery capacity, and improved software capabilities [1][2][3]. Mobile devices are being applied in non-traditional areas, such as medicine, physiotherapy, and informatics [4][5][6][7]. ...
Article
Full-text available
The widespread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests' duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, duration, and other involuntary and dangerous patients' movements after stroke. That provides an opportunity to measure different parameters that can be later fed into machine learning models for analyzing more complex patterns.
... They are handy because they are portable and small, allowing their correct positioning for different measurements [9][10][11]. These devices are equipped with different sensors, but more sensors can be connected through over-the-air connections [12][13][14][15][16][17][18]. These devices with increasing number of functionalities, and the number of available sensors, boost the options for creation of systems that could assist older adults [15,[19][20][21][22]. ...
Article
Full-text available
Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.
Chapter
Background/Objectives: This chapter presents current active ageing practice in multidisciplinary gerontology in Asia and Oceania. Method: We conducted a systematic review of research work done in Asia and Oceania in understanding the determinant factors on active ageing with a focus on mindset and behavioural changes. Keyword frequency analyses were used to identify the technology development determining factors on active ageing. Results: Four main technology solutions had been identified and developed for active ageing by practitioners and policymakers. With a total of 59 papers examined, the following areas were studied: (i) Exergaming improves physical activity levels mainly focuses on usage on cognition (N=8), walking gait (N=2), and falling (N=2); (ii) The major health wearables were meant to address issues relating to cognition (N=4), motor (N=3), and monitoring (N=2); (iii) The smartphones helped older adults with speech recognition (N=5), fitness (N=9) and food (N=2); and (iv) web-based programmes aimed to improve the cognition (N=9), depression (N=3) and education (N=10). Conclusion: The technological devices and systems have been developed in astronomical numbers in recent years to assist a growing older population on varying use scenarios for active ageing. These determinant factors are key to the mindset of behaviour changes. To better inform active ageing, it is important to investigate the feasibility of these determinant factors on technology developments in the Asia and Oceania region in the future.
Article
Full-text available
Abstract Background Despite the high usage of mobile phones in daily life in developing countries like Bangladesh, the adoption and usage of mHealth services have been significantly low among the elderly population. When searching previous studies, the researchers have found that no studies have empirically investigated whether the quality of life and service quality are significant for mHealth adoption by elderlies in Bangladesh. Hence, this study aimed to extend the Unified Theory of Acceptance and Use of Technology by adding service quality and the quality of life to empirically find the key factors that influence elderlies’ intention to adopt and use mHealth services in Bangladesh. Methods A face-to-face structured questionnaire survey method was used to collect data from 493 elderlies (aged 60 years and above) in Bangladesh. The data were analyzed with the Structural Equations Modelling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Results SEM results suggested that Social Influence, Hedonic Motivation, Price Value, Habit, and Service Quality had significant impact (p 0.05) effect on Behavioral Intention, which is inconsistent with existing literature. In addition, fsQCA findings suggest how the intensity of the influencers may contribute to high versus low m-health behavioral outcomes. Conclusions The findings have significant implications for theory, practice and future research as explained in the paper. The originality of this study is the integration of quality of life and service quality into UTUAT2 to explain the users’ behavioural intention and use behaviour. Overall, the findings may contribute to shaping appropriate policies for designing and implementing mHealth services effectively for elderlies in developing countries.
Article
The world is in a fast trend towards technology and digitalization. With technological developments, health literacy has advanced and the importance of digital health literacy in disease management has been realized. In order for a patient to understand the level of risk and predicted outcome of recovery in relation to a given diagnosis, it is important that they have access to comprehensive patient education resources that facilitate health literacy. In order to provide better health services and health education to the society, nurses should have a good level of digital health literacy. In order for nurses to use technology effectively, they need to acquire sufficient knowledge and skills about technology literacy. The purpose of this review is to emphasize the importance of digital health literacy that develops with technology and to develop a digital health literacy perspective in disease management.
Article
Background: Osteoporosis is the fourth most common chronic disease worldwide. The adoption of preventative measures and effective self-management interventions can help improve bone health. Mobile health (mHealth) technologies can play a key role in the care and self-management of patients with osteoporosis. Objective: This study presents a systematic review and meta-analysis of the currently available mHealth apps targeting osteoporosis self-management, aiming to determine the current status, gaps, and challenges that future research could address, as well as propose appropriate recommendations. Methods: A systematic review of all English articles was conducted, in addition to a survey of all apps available in iOS and Android app stores as of May 2021. A comprehensive literature search (2010 to May 2021) of PubMed, Scopus, EBSCO, Web of Science, and IEEE Xplore was conducted. Articles were included if they described apps dedicated to or useful for osteoporosis (targeting self-management, nutrition, physical activity, and risk assessment) delivered on smartphone devices for adults aged ≥18 years. Of the 32 articles, a random effects meta-analysis was performed on 13 (41%) studies of randomized controlled trials, whereas the 19 (59%) remaining studies were only included in the narrative synthesis as they did not provide enough data. Results: In total, 3906 unique articles were identified. Of these 3906 articles, 32 (0.81%) articles met the inclusion criteria and were reviewed in depth. The 32 studies comprised 14,235 participants, of whom, on average, 69.5% (n=9893) were female, with a mean age of 49.8 (SD 17.8) years. The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth-supported interventions resulted in a significant reduction in pain (Hedges g -1.09, 95% CI -1.68 to -0.45) and disability (Hedges g -0.77, 95% CI -1.59 to 0.05). The posttreatment effect of the digital intervention was significant for physical function (Hedges g 2.54, 95% CI -4.08 to 4.08) but nonsignificant for well-being (Hedges g 0.17, 95% CI -1.84 to 2.17), physical activity (Hedges g 0.09, 95% CI -0.59 to 0.50), anxiety (Hedges g -0.29, 95% CI -6.11 to 5.53), fatigue (Hedges g -0.34, 95% CI -5.84 to 5.16), calcium (Hedges g -0.05, 95% CI -0.59 to 0.50), vitamin D intake (Hedges g 0.10, 95% CI -4.05 to 4.26), and trabecular score (Hedges g 0.06, 95% CI -1.00 to 1.12). Conclusions: Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be valuable tools for patients and health professionals. However, most of the apps that are currently available lack clinically validated evidence of their efficacy and focus on a limited number of symptoms. A more holistic and personalized approach within a cocreation design ecosystem is needed. Trial registration: PROSPERO 2021 CRD42021269399; https://tinyurl.com/2sw454a9.
Article
Full-text available
Objective To determine the reliability of three physical performance tests performed via a telehealth visit (30-second arm curls test, 30-second chair stand test, 2-minute step test) among community-dwelling older Veterans. Design Cross sectional study. Setting Virtual. Participants Veterans (mean age 75) who enrolled in Gerofit, virtual group exercise program. Interventions Not applicable. Main outcome measures Participants were tested by two different assessors at one time point. The Interclass Correlation Coefficient (ICC) with 95% confidence intervals (CIs) and Bland-Altman plots were used as measures of reliability. To assess generalizability, ICCs were further evaluated by health conditions (type 2 diabetes, arthritis, obesity, and depression). Results Assessments were conducted among 55 participants. The ICC was above 0.98 for all three tests across health conditions and Bland-Altman plots indicated that there were no significant systematic errors in the measurement. Conclusions The virtual physical performance measures appear to have high reliability and the findings are generalizable across health conditions among Veterans. Thus, they are reliable for evaluating physical performance in older Veterans in virtual settings.
Conference Paper
Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such deficiencies are conventionally measured subjectively by geriatrics using questionnaire-based methods and clinical tests. Activities of daily living are also assessed in clinical settings by analysing simple tasks performed by the subject such as sit to stand and walking some distances. The clinical methods used to assess frailty and analyse the activity of daily living are subjective in nature and prone to human error. An objective method is proposed to quantitatively measure frailty using inertial sensor mounted on healthy, frail and nonfrail subjects while performing the sit to stand test (SiSt). An artificial neural networks based algorithm is developed to classify the frailty by extracting a unique set of features from 2D -Centre of Mass (CoM) trajectories derived from SiSt clinical test. The results indicate that the proposed algorithms provides an objective assessment of frailty that can be used by geriatrics in turn to make a more objective judgement of frailty status of older people.
Article
Full-text available
Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55–51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14–8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ PA levels and functional recovery during hospitalization.
Article
Full-text available
Background: Falls are the leading cause of injury-related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer a high potential to provide fall risk screening for older adults in home settings. However, there is limited understanding of whether smartphone technology for falls screening is usable by older adults who present age-related changes in perceptual, cognitive, and motor capabilities. Objective: The aims of this study were to develop a fall risk mobile health (mHealth) app and to determine the usability of the fall risk app in healthy, older adults. Methods: A fall risk app was developed that consists of a health history questionnaire and 5 progressively challenging mobility tasks to measure individual fall risk. An iterative design-evaluation process of semistructured interviews was performed to determine the usability of the app on a smartphone and tablet. Participants also completed a Systematic Usability Scale (SUS). In the first round of interviews, 6 older adults participated, and in the second round, 5 older adults participated. Interviews were videotaped and transcribed, and the data were coded to create themes. Average SUS scores were calculated for the smartphone and tablet. Results: There were 2 themes identified from the first round of interviews, related to perceived ease of use and perceived usefulness. While instructions for the balance tasks were difficult to understand, participants found it beneficial to learn about their risk for falls, found the app easy to follow, and reported confidence in using the app on their own. Modifications were made to the app, and following the second round of interviews, participants reported high ease of use and usefulness in learning about their risk of falling. Few differences were reported between using a smartphone or tablet. Average SUS scores ranged from 79 to 84. Conclusions: Our fall risk app was found to be highly usable by older adults as reported from interviews and high scores on the SUS. When designing a mHealth app for older adults, developers should include clear and simple instructions and preventative strategies to improve health. Furthermore, if the design accommodates for age-related sensory changes, smartphones can be as effective as tablets. A mobile app to assess fall risk has the potential to be used in home settings by older adults.
Article
Full-text available
BACKGROUND: Older patients with cancer are at an increased risk of adverse outcomes. A geriatric assessment (GA) is a compilation of reliable and validated tools to assess domains that are predictors of morbidity and mortality, and it can be used to guide interventions. However, the implementation of GA and GA-driven interventions is low due to resource and time limitations. GA-driven interventions delivered through a mobile app may support the complex needs of older patients with cancer and their caregivers. OBJECTIVE: We aimed to evaluate the feasibility and usability of a novel app (TouchStream) and to identify barriers to its use. As an exploratory aim, we gathered preliminary data on symptom burden, health care utilization, and satisfaction. METHODS: In a single-site pilot study, we included patients aged ≥65 years undergoing treatment for systemic cancer and their caregivers. TouchStream consists of a mobile app and a Web portal. Patients underwent a GA at baseline with the study team (on paper), and the results were used to guide interventions delivered through the app. A tablet preloaded with the app was provided for use at home for 4 weeks. Feasibility metrics included usability (system usability scale of >68 is considered above average), recruitment, retention (number of subjects consented who completed postintervention assessments), and percentage of days subjects used the app. For the last 8 patients, we assessed their symptom burden (severity and interference with 17-items scored from 0-10 where a higher score indicates worse symptoms) using a clinical symptom inventory, health care utilization from the electronic medical records, and satisfaction (6 items scored on a 5-point Likert Scale for both patients and caregivers where a higher score indicates higher satisfaction) using a modified satisfaction survey. Barriers to use were elicited through interviews. RESULTS: A total of 18 patients (mean age 76.8, range 68-87) and 13 caregivers (mean age 69.8, range 38-81) completed the baseline assessment. Recruitment and retention rates were 67% and 80%, respectively. The mean SUS score was 74.0 for patients and 72.2 for caregivers. Mean percentage of days the TouchStream app was used was 78.7%. Mean symptom severity and interference scores were 1.6 and 2.8 at preintervention, and 0.9 and 1.5 at postintervention, respectively. There was a total of 27 clinic calls during the intervention period and 15 during the postintervention period (week 5-8). One patient was hospitalized during the intervention period (week 1-4) and two patients during the postintervention period (week 5-8). Mean satisfaction scores of patients and caregivers with the mobile app were 20.4 and 23.4, respectively. Barriers fell into 3 themes: general experience, design, and functionality. CONCLUSIONS: TouchStream is feasible and usable for older patients on cancer treatment and their caregivers. Future studies should evaluate the effects of the TouchStream on symptoms and health care utilization in a randomized fashion.
Article
Full-text available
Background: The increasing use of Web-based solutions for health prevention and promotion presents opportunities to improve self-management and adherence to guideline-based therapy for individuals with type 2 diabetes (T2DM). Despite promising preliminary evidence, many users stop using Web-based solutions due to the burden of data entry, hidden costs, loss of interest, and a lack of comprehensive features. Evaluations tend to focus on effectiveness or impact and fail to evaluate the nuanced variables that may interact to contribute to outcome success (or failure). Objective: This study aimed to evaluate a Web-based solution for improving self-management in T2DM to identify key combinations of contextual variables and mechanisms of action that explain for whom the solution worked best and in what circumstances. Methods: A qualitative realist evaluation was conducted with one-on-one, semistructured telephonic interviews completed at baseline, and again toward the end of the intervention period (3 months). Topics included participants' experiences of using the Web-based solution, barriers and facilitators of self-management, and barriers and facilitators to effective use. Transcripts were analyzed using thematic analysis strategies, after which the key themes were used to develop statements of the relationships between the key contextual factors, mechanisms of action, and impact on the primary outcome (glycated hemoglobin, HbA1c). Results: Twenty-six interviews (14 baseline, 12 follow-up) were completed with 16 participants with T2DM, and the following 3 key groups emerged: the easiest fit, the best fit, and those who failed to activate. Self-efficacy and willingness to engage with the solution facilitated improvement in HbA1c, whereas competing priorities and psychosocial issues created barriers to engagement. Individuals with high baseline self-efficacy who were motivated, took ownership for their actions, and prioritized diabetes management were early and eager adopters of the app and recorded improvements in HbA1cover the intervention period. Individuals with moderate baseline self-efficacy and no competing priorities, who identified gaps in understanding of how their actions influence their health, were slow to adopt use but recorded the greatest improvements in HbA1c. The final group had low baseline self-efficacy and identified a range of psychosocial issues and competing priorities. These participants were uncertain of the benefits of using a Web-based solution to support self-management, ultimately resulting in minimal engagement and no improvement in HbA1c. Conclusions: Self-efficacy, competing priorities, previous behavior change, and beliefs about Web-based solutions interact to determine engagement and impact on the clinical outcomes. Considering the balance of these patient characteristics is likely to help health care providers identify individuals who are apt to benefit from a Web-based solution to support self-management of T2DM. Web-based solutions could be modified to incorporate the existing screening measures to identify individuals who are at risk of suboptimal adherence to inform the provision of additional support(s) as needed.
Article
Full-text available
In 2009, we published a paper in which we showed how three independent sources of data indicated that, rather than being a unidimensional measure of perceived usability, the System Usability Scale apparently had two factors: Usability (all items except 4 and 10) and Learnability (Items 4 and 10). In that paper, we called for other researchers to report attempts to replicate that finding. The published research since 2009 has consistently failed to replicate that factor structure. In this paper, we report an analysis of over 9,000 completed SUS questionnaires that shows that the SUS is indeed bidimensional, but not in any interesting or useful way. A comparison of the fit of three confirmatory factor analyses showed that a model in which the SUS's positive-tone (odd-numbered) and negative-tone (even-numbered) were aligned with two factors had a better fit than a unidimensional model (all items on one factor) or the Usability/Learnability model we published in 2009. Because a distinction based on item tone is of little practical or theoretical interest, we recommend that user experience practitioners and researchers treat the SUS as a unidimensional measure of perceived usability, and no longer routinely compute Usability and Learnability subscales.
Article
Full-text available
The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing.
Article
Full-text available
Introduction People with mobility limitations can benefit from rehabilitation programmes that provide a high dose of exercise. However, since providing a high dose of exercise is logistically challenging and resource-intensive, people in rehabilitation spend most of the day inactive. This trial aims to evaluate the effect of the addition of affordable technology to usual care on physical activity and mobility in people with mobility limitations admitted to inpatient aged and neurological rehabilitation units compared to usual care alone. Methods and analysis A pragmatic, assessor blinded, parallel-group randomised trial recruiting 300 consenting rehabilitation patients with reduced mobility will be conducted. Participants will be individually randomised to intervention or control groups. The intervention group will receive technology-based exercise to target mobility and physical activity problems for 6 months. The technology will include the use of video and computer games/exercises and tablet applications as well as activity monitors. The control group will not receive any additional intervention and both groups will receive usual inpatient and outpatient rehabilitation care over the 6-month study period. The coprimary outcomes will be objectively assessed physical activity (proportion of the day spent upright) and mobility (Short Physical Performance Battery) at 6 months after randomisation. Secondary outcomes will include: self-reported and objectively assessed physical activity, mobility, cognition, activity performance and participation, utility-based quality of life, balance confidence, technology self-efficacy, falls and service utilisation. Linear models will assess the effect of group allocation for each continuously scored outcome measure with baseline scores entered as a covariate. Fall rates between groups will be compared using negative binomial regression. Primary analyses will be preplanned, conducted while masked to group allocation and use an intention-to-treat approach. Ethics and dissemination The protocol has been approved by the relevant Human Research Ethics Committees and the results will be disseminated widely through peer-reviewed publication and conference presentations. Trial registration number ACTRN12614000936628. Pre-results.
Article
Introduction: In an ageing population, increasing chronic disease prevalence puts a high economic burden on society. Physical activity plays an important role in disease prevention and should therefore be promoted in the elderly. Methods: In this study, a mobile health (mHealth) system was implemented in a care home setting to monitor and promote elderly peoples' daily activity. The physical activity of 20 elderly people (8 female and 12 male, aged 81 ± 9 years old) was monitored over 10 weeks using the mHealth system, consisting of a smartphone and heart rate belt. Feedback on physical activity was provided weekly. A reference performance test battery derived from the Senior Fitness Test determined the participants' physical fitness. Results: Activity levels increased from week 1 onwards, peaking at week 5, and decreasing slightly until week 10. This illustrates that the use of mHealth and feedback on physical activity can motivate the elderly to become more active, but that the effect is transient without other incentives. Bio-data from the mHealth system were translated into a fitness score explaining 65% of the test battery's variance. After separating the elderly into three groups depending on physical fitness determined from the test battery, classification based on the fitness score resulted in a correct classification rate of 67.3%. Discussion: This study demonstrates that an mHealth system can be implemented in a care home setting to motivate activity of the elderly, and that the bio-data can be translated in a fitness score predicting the outcome of labour-intensive tests.
Article
With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1. s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.