JMIR FORMATIVE RESEARCH

Alvarez-Romero et al

Original Paper

Machine Learning–Based Prediction of Changes in the Clinical
Condition of Patients With Complex Chronic Diseases: 2-Phase
Pilot Prospective Single-Center Observational Study
Celia Alvarez-Romero1, MSc; Alejandro Polo-Molina2, MSc; Eugenio Francisco Sánchez-Úbeda2, PhD; Carlos
Jimenez-De-Juan3, MSc; Maria Pastora Cuadri-Benitez3, MSc; Jose Antonio Rivas-Gonzalez1, BSc; Jose Portela2,
PhD; Rafael Palacios2, PhD; Carlos Rodriguez-Morcillo2, PhD; Antonio Muñoz2, PhD; Carlos Luis Parra-Calderon1,
MSc; Maria Dolores Nieto-Martin3, PhD; Manuel Ollero-Baturone3, PhD; Carlos Hernández-Quiles3, PhD
1Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones
Científicas, University of, Seville, Spain
2

Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, Madrid, Spain

3

Internal Medicine Department, Virgen del Rocio University Hospital, Sevilla, Spain

Corresponding Author:
Carlos Hernández-Quiles, PhD
Internal Medicine Department
Virgen del Rocio University Hospital
Av Manuel Siurot s/n
Sevilla, 41013
Spain
Phone: 34 697950012
Email: quiles_es@yahoo.es

Abstract
Background: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases.
A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic andtherapeutic actions that stop the exacerbation of the disease.
Objective: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting
the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected throughan internet of things mobility device.
Methods: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients wererecruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A(BI≤20; total dependence), class B (20<BI≤60; severe dependence), and class C (BI>60; moderate or mild dependence, or
independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used
to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute
error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS andPython for the machine learning modeling.
Results: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20;
and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The
mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others
required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity,
85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria,
and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73,82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78)
of patients. The most important variables for predicting the BI were identified as the maximum step count during evening andmorning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a
median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the
BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively.
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Conclusions: Using commercially available mobility recording devices makes it possible to identify different mobility patterns
and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.
(JMIR Form Res 2024;8:e52344) doi: 10.2196/52344
KEYWORDS
patients with complex chronic diseases; functional impairment; Barthel Index; artificial intelligence; machine learning; prediction
model; pilot study; chronic patients; chronic; development study; prognostic; diagnostic; therapeutic; wearable; wearables;
wearable activity tracker; mobility device; device; physical activity; caregiver

Introduction
The Spanish strategy for the approach to chronicity in the
National Health System defines patients with complex chronic
diseases as patients with 1 or more chronic diseases that present
greater complexity in their management due to changing needs
that force continuous evaluations and make necessary thecoordinated use of various care levels and, in some cases, healthand social [1]. Social changes and health advances mean that
we are living longer and better and that most diseases affecting
us are becoming chronic. Several of them are accumulating,which causes the growing phenomenon of people living with
polypathology or complex chronic diseases. This concept
includes not only people with the primary disease that triggers
other secondary conditions but also those people where 2 ormore chronic diseases coexist. It is a population characterized
by frailty, polymedication, old age, hyperfrequent use of
emergency services, and frequent re-entering. It is estimatedthat 70% to 95% of the older people in our environment have
1.2 to 4.2 chronic diseases, which constitute the leading deathcause in the world (60% of the total) [2]. These patients generate
a greater demand for attention in different care settings and use
a more significant number of health and social resources. It is
predominantly seen in older patients presenting with limiting
and progressive diseases (eg, renal or cardiac insufficiency),
polypharmacy, and some degree of functional impairment [3].
Functional impairment is one of the most decisive prognostic
factors in patients with complex chronic diseases. A moresignificant functional impairment indicates that the disease isprogressing, which requires implementing diagnostic andtherapeutic actions that stop the exacerbation of the disease.
The functional assessment of patients with complex chronic
diseases can be performed using tools such as the Barthel Index(BI) [4], mobility tests, the 4-meter gait test [5], the balance test[6], and the timed “up and go” test [7].
The BI has excellent predictive value for variables such asmortality, hospital admission, and stay length in rehabilitation
departments. In addition, it is an indicator to assess thefunctional and prognostic capacities of patients with complex
chronic diseases [8,9]. The BI is a simple measure developedon empirical bases in obtaining and interpreting it. It is about
assigning, to each patient, a score based on their degree of
dependence to perform a series of basic activities related mainly
to the individual’s mobility (eg, moving between the chair andthe bed, moving, going up and down stairs, or showering). The
total score can vary between 0 (fully dependent) and 100 points(completely independent) [10].

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Concerning functional capacity, physical inactivity is definedas the spectrum of any decrease in body movement that reduces
energy expenditure toward the baseline level. Physical inactivity
affects many aspects of a person, such as respiratory capacity,bones, or the central nervous system, among others, and caneven lead to various diseases [11]. In addition, physicalinactivity itself decreases the physical fitness of the person, theduration of good health, and the age of onset of his or her firstchronic illness. Relative to this, there are several parameters toassess the physical inactivity of the person, such as the numberof daily steps, the time spent sitting, or the immobilization of
the limbs, among others.
On the other hand, the possible causal relationship between
sedentary behavior and mortality due to various causes has beenstudied. Various studies used accelerometers on the thigh tocontrol the body’s position, and the chances of experiencing
illnesses increased for every additional hour of sitting. Regarding
limb immobilization in older people, one of the main concerns
is the inability to recover the loss of bone strength and muscle
mass [12].
Recent technological advances allow mobility monitoring
through smartphones or wrist devices, which are widelydistributed throughout the population. These devices provide
information on the paths, the number of steps, the speed of themarch, and the periods of falls, among others. Specific initiatives
have tried to apply this information to the health sector. For
instance, a multiagent system equipped with sensors has beendeveloped to collect vital signs from patients. This system isintended to facilitate various tasks within the residences of older
or disabled people [13]. Additionally, mobility monitoring by
sensors in different rooms of the house has been considered to
study translations between rooms and measure the length ofstay in each room for older patients living alone [14].
Furthermore, individual physical activity can be monitored
using accelerometers placed on the patient’s trunk and thigh[15]. At the same time, smartwatches have been used to evaluate
movement and gait patterns in patients with Parkinson disease
and essential tremor [16]. These advancements are driving thedevelopment of more hardware devices to enhance health caredelivery and turn the concept of “a doctor in your pocket” intoa reality for patients.
We would like to emphasize that using sensors to obtain healthinformation currently has a specific trajectory [17-19]. Mobility
has long presented prominent importance when dealing withdiseases whose onset and symptomatic progression affect thefunctional capacity of the subject [20]. Recently, machinelearning (ML) techniques are increasingly being considered to
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characterize the movement, or some particularities of the
movement, which can provide relevant information about thepatient’s clinical status [21,22]. Some works investigate therelationships between movement and specific clinicalpathologies [23,24].
Despite these initiatives, the evaluation of the mobility ofpatients with complex chronic diseases and their relationshipwith the functional capacity measured by the BI has yet to be
explored [25]. For all these reasons, and with this background,
this study aims to develop and validate mobility patterns basedon artificial intelligence and the internet of things (IoT)
environment, aiming to predict changes in the clinical conditionof patients with complex chronic diseases through the predictionof the BI to know the clinical and functional status of thepatients.

Methods
Study Design and Recruitment
This 2-phase pilot observational study has been designed toanalyze how mobility deterioration can reflect changes in thepatient’s clinical condition and possible degeneration in the
integrated care of patients with complex chronic diseases. To
this end, a prospective, single-center, descriptive study was
carried out.
Eligible patients met the criteria of chronic patients withcomplex health needs defined according to the Integrated Patient

Alvarez-Romero et al
Care Process of the Andalusian Ministry of Health [26].
Concretely, the study population included patients older than
65 years of age with multimorbidity (ie, diagnosed with at least
2 chronic diseases), and the recruitment took place at the Virgendel Rocio University Hospital of Seville, Spain. In addition,
those patients in a situation of agony or those whose vital
prognosis was limited, patients with psychiatric disease, andpatients or caregivers unable to use mobility devices were
excluded from the study. The study subjects were patients of
the Internal Medicine Department of the Virgen del Rocio
University Hospital of Seville, as part of the Andalusian HealthService, Spain.
The research was conducted in 2 phases. In the initial phase(January to November 2022), a cohort of 50 patients wasenrolled, and their BI was measured before the allocation of thewearable activity trackers (WATs), during routine doctor
appointments after providing informed consent. Approximately
1 month after the first assessment (encounter 1), the BI was
measured again (encounter 2) to evaluate any changes in theirfunctional status (Figure 1). The recruitment was conductedaccording to different degrees of patients with complex chronic
diseases dependence based on the BI measured during the firstassessment. In particular, the enrolled patient’s group wasclassified into 3 groups based on their BI scores: class A
included patients with BI≤20 (total dependence), class B
comprised patients with 20<BI≤60 (severe dependence), andclass C consisted of patients with BI>60 (moderate or mild
dependence, or independent) [27].

Figure 1. Gantt chart illustrating the temporal progression of the enrollment in the 2-phase clinical study. Each horizontal bar represents the beginningof the patient monitoring and the 1-month or 3-months span, depending on if it is phase 1 or phase 2, before the following BI measurements. BI: Barthel
Index; Q: quarter.

In the second phase (July 2022 to May 2023), 40 patients wererecruited. Similar to phase 1, patients were recruited duringdoctor appointments, and their BI was measured 3 months after
encounter 1 and encounter 2. Therefore, for phase 2 patients,
there was an additional encounter 3.
An IoT framework was deployed to gather patient mobility data
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market. The IoT-based infrastructure consisted of using mobile
devices and WAT to measure the mobility activities of patients,
considering the no or minimal invasion in the development of
the daily tasks for the patients under study. The WAT used inthis study recorded the step count, the cardiac activity, and thesleep duration from which both the step count and the heart rate
were analyzed (Figure 2).

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Figure 2. Illustration of data captured by the wearable activity trackers. The line charts show a specific patient’s step count and heart rate daily timeseries.

Once the 90 patients were included in the study, they wereassigned a WAT, and different mobility and functional status
tests were conducted. An information system for data storage(Analytics Datastore) was developed. This database allowed
both the dumping of the information collected through the WAT
and the storage of the relevant clinical information of thepatients extracted from the electronic health records (Figure 3).
For this purpose, confidentiality protocols of information andthe security of the center’s systems were followed and incompliance with the ethical approval obtained by the hospital’s
ethics committee.

Figure 3. Visual representation of the extract, transform, and load (ETL) process. Incorporating WAT-derived measurements, clinical records fromhealth care systems, and patient-specific data into the analytics datastore used for the model training. EHR: electronic health record; WAT: wearable
activity tracker.

The study patients’ exposure and clinical variables of interest
were analyzed to characterize patient groups regarding mobility,
using mobility measurement devices and clinical conditions.
Demographic and clinical variables such as diseases, fragility,and polypathology criteria; pharmacological variables; andfunctional tests such as the BI, balance test, and timed “up andgo” test were collected. Statistical analysis was performed using
SPSS Statistics software (version 25; IBM Corp) and Python(version 3.10.9; Python Software Foundation) for the MLmodeling.

Data Preparation for the ML Model
The WAT automatically gathered continuous and noninvasive
data on a range of parameters, encompassing heart rates, step
counts, and sleep duration. However, these raw data must beprocessed to apply ML techniques. Furthermore, given thepotential influence of the walking aids on mobility patterns,
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patients were classified into 3 distinct groups. The first groupencompassed patients using wheelchairs; the second comprised
individuals using canes, walkers, or receiving aid from a
caregiver during ambulation; and the third consisted of thosewith no reliance on assistance.
To ensure high data quality, instances where the median heart
rate is missing are identified as null, along with thecorresponding count of steps. The steps taken within 1-hourintervals are aggregated, and the median heart rate for thesehourly intervals is computed. The resulting time series data ofhourly step counts are then smoothed by applying a centeredrolling window with a window size of 3. Subsequently, the data
are grouped by the specific hour, resulting in an average
representation of each patient’s activity throughout a 24-hour
period. The data used to generate the mean activity profile
consist of the information recorded during the 30-day period
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before encounter 2 requiring at least 14 days’ worth of data toconsider the patient in the data set. This approach aims todevelop a methodology that allows the estimation of the BI at
any given moment using the information collected by the WAT
over the last 30 days. Therefore, it holds the potential to provide
a more dynamic and real-time assessment of the BI based oncontinuous monitoring through WAT. Additionally, mobility
profiles from encounter 3 served as a production-like test set
and were excluded from the model’s training. This strategy
evaluated the model’s real-world performance and generalizationon unseen data.
The 24-hour mean activity profiles were partitioned into 4-time
segments: morning (7 AM-1 PM), afternoon (2 PM-7 PM),evening (8 PM-11 PM), and overnight (midnight-6 AM). Thismethodology aimed to reduce the dimensionality of the data
inputted into the model. Various approaches were considered
to reduce dimensionality, including summing the steps withineach interval, calculating the mean, and determining the
maximum value. The 4-time segments were selected based on
the findings of Polo-Molina et al [25], where it wasdemonstrated that mobility patterns can be categorized into
distinct clusters. The study highlighted that the maximum valueof steps within each interval aligns with the suggested division,
regardless of individual variations in the mobility patterns.
Once the data set was generated, it was divided into training
and validation sets, with 70% (n=63) of the records allocatedfor training and 30% (n=27) for validation. To ensure that the
proportions of each group of walking aids were maintained at
this ratio, the division was performed within each group, andthen the data were combined to create the final training andvalidation data sets. This approach aimed to ensure
representative and well-balanced distributions of walking aids
in both sets, allowing for robust evaluation of the model’s
performance across different modes of mobility.

Explainable ML Model
A decision tree regressor has been considered to predict the BI.
Decision trees iteratively select variables to maximize
information gain or minimize impurity at each decision node,creating a hierarchical structure. Therefore, starting from thewhole set of variables, at each split, the training algorithm
selects the variable that generates the best split [28].
Moreover, to optimize the performance of the regression model,
the hyperparameters were fine-tuned using a cross-validation
approach with 7 folds. This technique ensures robustness andselects the optimal settings that yield the best predictive accuracy
for the BI. The cross-validation optimization considered thehyperparameters “min_impurity_decrease” (ranging from 0.0
to 1.0 in increments of 0.01), “min_samples_leaf” (from 1 to
10 in steps of 1), and “min_samples_split” (spanning 1 to 10
with an interval of 1).
In addition, to assess the quality of the predictions, several
metrics were considered, including the mean absolute error,
median absolute error (MAD), and root mean squared error.
Finally, the permutation importance from explanatory variables
was computed by permuting individual feature values whilemeasuring the subsequent decline in model performance [28].
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Alvarez-Romero et al
This iterative process assigned a score to each feature based on
the decrease in predictive power caused by permutations, withelevated scores indicating significant contributions to accurate
predictions.

Ethical ConsiderationsFirst, ethical approval was obtained in the health organization
based on the regional regulations before involving it in the study
execution. Likewise, informed consent procedures were defined,
including informed consent and information sheets for thepatients who were included in the study. Before starting thisstudy, and based on the ethical and legal regulations, ethicalapproval was requested from the Ethics Committee of the Virgendel Rocio University Hospital of Seville, Spain. The study
protocol, informed consent documents, and information sheets
were submitted, and approval from the ethics committee wasreceived. The study began, and patients who met the inclusion
criteria were invited to participate after explaining the study
procedures. Those who accepted and signed the informed
consent and information sheets were included in the clinicalstudy.
In addition, to ensure the protection of the privacy andconfidentiality of the study participants, sensible data wereanonymized and deidentified. Likewise, confidentiality protocols
of information and the security of the center’s systems were
followed and in compliance with the ethical approval obtainedby the hospital’s ethics committee.

Results
Statistical Analysis
A total of 90 patients were included in the study and wereclassified into 3 categories according to their BI. Concretely,
50 patients were enrolled in the first phase (10 in the BI classA, 20 in BI class B, and 20 in BI class C), and in the second
phase, 40 patients were included (20 patients in BI class B and
20 patients in BI class C).
Of the patients, 94% (n=84) had a caregiver, of which 40%(n=34) had a son or a daughter, 32% (n=27) had a spouse, 17%(n=14) had other relatives, and 11% (n=9) had a professional
caregiver. The mean value of functional capacity measured by
the BI was 58.31 (SD 24.5). Concerning mobility aids, 58%(n=52) of patients did not require it, 20% (n=18) required awalker, 17% (n=15) a wheelchair, 4% (n=4) required a cane,and 1% (n=1) required crutches. The clinical complexity washigh with 76 (85%) patients meeting the criteria for patientswith polypathology, with a mean of 2.7 (SD 1.25), and 19 (21%)
patients met the criteria for patients with complex chronic
diseases. A total of 61 (69%) patients met the frailty criteria.
The most characteristic symptoms of this population were
dyspnea (n=73, 82%), with 47% (n=42) of patients requiring
home oxygen therapy; chronic pain (n=63, 70%); asthenia(n=61, 68%); and anxiety (n=41, 46%; Table 1). The mean
number of drugs taken chronically was 12.19 (SD 11.88), with87% (n=78) meeting the polypharmacy criteria and 70% (n=63)
meeting the extreme polypharmacy criteria. Psychotropic drugs
were the most consumed pharmacological group (n=29, 33%).
Five patients died during the study.
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Table 1. Description of the baseline characteristics of the study in line with the mobility classification.
Characteristics

Mobility classificationBIa ≤20 (total
dependence)

20<BI≤60 (severe dependence)

Phase 1(n=20)

Phase 2(n=20)

P valuePhase 1(n=20)

Phase 2(n=20)

P valueAge (years), mean (SD)72.56 (14.5)

79.6 (8)75.35 (8.9).49

78.65 (6.5) 75.2 (6.8).90Gender (women), n (%)

8 (80)

11 (55)9 (45).40

8 (40)

7 (35).74

Caregiver, n (%)

10 (100)

20 (100)

20 (100).12

16 (80)19 (95).15

BI, mean (SD)13.89 (4.8)

42.25 (10.12)

50.25 (10.44).89

80 (12.1)

80.75 (11.2).54

Balance test, mean (SD)0 (0)1.4 (2.1)

1.6 (1.46).74

4.6 (1.39)

4 (1.65).0930-second chair stand and go, n (%)

>.99

>.99

0 points

10 (100)

16 (80)16 (80)16 (80)16 (80)1 point

0 (0)4 (20)

4 (20)

4 (20)

4 (20)

Timed “up and go” test, n (%)

.47

.06

No frailty

0 (0)

0 (0)

1 (5)

15 (75)8 (40)b

8 (80)

11 (55)10 (50)

0 (0)

1 (5)

Frailty

2 (20)

9 (45)

9 (45)

5 (25)

11 (55)Frailty criteria, n (%)

7 (70)

14 (70)16 (80).46

12 (60)13 (65).74

PPc criteria, n (%)

10 (100)

19 (95)18 (90).32

14 (70)16 (80).64

Patient with polypathology categories, mean (SD) 3.3 (1)2.89 (1.15)

3 (1.48).23

2.3 (1.08)

2.35 (1.22).98

PCCDd, n (%)

4 (40)

4 (20)

2 (10).08

17 (85)17 (85)>.99

Dyspnea, n (%)

5 (50)

17 (85)18 (90).78

18 (90)17 (85).33

Home oxygen, n (%)

4 (40)

7 (35)

7 (35).45

9 (45)

12 (60).26

Chronic pain, n (%)

8 (80)

9 (45)

3 (15).001

11 (55)7 (35).2

Pressure ulcer, n (%)

6 (60)

2 (10)

1 (5)

.54

0 (0)

2 (10).15

Insomnia, n (%)

5 (50)

10 (50)7 (35).17

10 (50)8 (40).37

Anxiety, n (%)

8 (80)

9 (45)

10 (50).66

7 (35)

7 (35)<.99

Asthenia, n (%)

8 (80)

14 (70)15 (75).49

12 (60)12 (60)<.99

Anorexia, n (%)

40 (40)8 (40)

6 (30).21

3 (15)

5 (25).12

Nausea and vomiting, n (%)

3 (30)

5 (25)

2 (10).12

2 (10)1 (5).29

Diarrhea, n (%)

4 (40)

2 (10)

1 (5)

.23

1 (5)

1 (5)

<.99

Number of drugs, mean (SD)13.11 (3.6)

11.85 (3.6)

12.5 (3.2).86

11.8 (3.3)

12.2 (3.6).44

Polypharmacy, n (%)

10 (100)

20 (100)

20 (100)<.99

19 (95)19 (95)<.99

Extreme polypharmacy, n (%)

8 (80)

15 (75)16 (80).46

15 (75)16 (80).43

Death, n (%)

2 (20)

2 (10)

0 (0)

.14

1 (5)

0 (0)

.31

HRF

a

BI>60 (moderate or mild dependence,or independent)

BI: Barthel Index.

b

HRF: high risk of fall.

c

PP: polypathological patient.

d

PCCDs: patients with complex chronic diseases.

Regarding the baseline characteristics, comparing the different
phases 1 and 2 categories, significant differences were only
found in the presence of pain in the classification BI class B(Table 1). In that category, the mean BI at the beginning of thestudy was 50.25 (SD 10.44), with an increase at the end of thehttps://formative.jmir.org/2024/1/e52344

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study to 63.53 (SD 28.92). In BI class C, an initial BI of 80.75(SD 11.27) and a final BI of 86.76 (SD 15.806) were observed.
In the initial balance test for BI class B, the value was 1.6 (SD1.46) points and 4.8 (SD 1.61) points at the end of the study,

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while in the BI class C, the initial value was 4 (SD 1.6) and thefinal value was 5.5 (SD 2.1).

Data for the ML Model
Following the aforementioned methodology, the patient cohort
was reduced to 54 patients, taking into account only those who
had at least 14 days’ worth of records before the doctor’s
appointment. The principal factors contributing to this lack ofdata were predominantly attributed to mortality or patients
becoming bedridden, subsequently ceasing to use the wristband.

Mobility profiles corresponding to encounter 3 were used as a
production-like test in a cohort of 21 patients. The best model
results for defining the average 24-hour activity profile wereobtained using the maximum value of steps in each interval andthe type of walking aid if needed. Table 2 presents the complete
set of variables used for training the model. The mean BI in the
training and validation sets are 66.5 (SD 23.4) and 65.0 (SD24.1), respectively. In contrast, the mean value in the test set isnotably higher at 85.0 (SD 22.5).

Table 2. Description of the candidate features used to train the regression tree model.
Variable name

Description

morning_max

Maximum number of steps recorded during the morning period

afternoon_max

Maximum number of steps recorded during the afternoon period

evening_max

Maximum number of steps recorded during the evening period

overnight_max

Maximum number of steps recorded during the overnight period

no_walking_aid

Avoidance of any type of walking aid

cane_or_walker

Use of either a cane, walker, or caregiver’s help for walking

wheelchair

Use of wheelchair

The fitted model, whose parameters were selected throughcross-validation, is a decision tree regressor with a depth of 3,
minimum impurity decrease of 0.0, minimum samples in a leaf
node of 2 and minimum samples in a split of 9 (Figure 4).
Among the features considered, the most important variables
for predicting the BI were identified as the maximum step countduring the evening and morning periods, and the absence of amobility device. These key predictors were determined basedon their significant impact on the functional status of the patients(Figure 5 [28]).
Based on the results in Table 3, the model exhibits consistency
in MAD with a value close to 5 in the training, validation, andtest sets. Furthermore, according to Figure 6, when observing
the predicted values compared to the real ones, the model does
not present a significant difference between the predicted andthe real BI.

Once the BI prediction was performed, the intervals defining
each BI class were further considered. Subsequently, aclassification prediction is carried out by converting thepredicted value into its corresponding class label, thereby
assigning the appropriate class to the given BI prediction. As
observed in Table 3 and Figure 7, in the training set, the model
achieved precision, recall, and F1-scores of 0.88, 0.93, and 0.90
for class B, respectively. For class C, the model obtained
precision, recall, and F1-scores of 0.94 for all 3 measures.
However, for class A, all the metrics were 0.00 due to the limited
support for that class (only 1 instance). In the validation set, themodel demonstrated consistent performance with precision,
recall, and F1-scores of 0.88 for both class B and class C. On
the other hand, in the data coming from the test set, the model
achieved precision, recall, and F1-scores of 0.5, 1, and 0.67 forclass B, respectively. For class C, the model obtained precision,
recall, and F1-scores of 1, 0.94, and 0.97, respectively. Finally,
only 1 member from class A was predicted as class B.

Figure 4. Regression tree model visualization detailing the decision paths within the fitted model. Nodes represent decision points, indicating thesample sizes at each node and the square error committed if the value considered is as the prediction.

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Figure 5. Descending variable importance ranking for the fitted model. Each violin plot represents the distribution of the decrease in R2 score whena single feature value, represented in the y-axis, is randomly shuffled. The importances of the permutations are labeled A (Train set), B (Validation set)
and C (Test set).

Table 3. Training, validation, and test errors and accuracy of the model when transforming the predicted values to class A, B, and C.

a

Train

Validation

Test

Accuracy

91%

88%

90%

MAE

a

6.30

8.75

10.10

MADb

5.42

4.50

6.00

RMSEc

8.13

12.99

14.05

MAE: mean absolute error.

b

MAD: median absolute error.

c

RMSE: root mean squared error.

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Figure 6. Comparative of real versus predicted values for training (A), validation (B), and test (C) ordered by increasing Barthel Index values. The
horizontal axis of the graphs represent the values for the different subjects of the study. To de-identify them, CIOT (Chronic patient intenet of things)
project identifiers were used, followed by the B (patients with Barthel Index >20 and &#8804; 60 [severe dependence]) or C (patients with Barthel
Index >60 [moderate or mild dependence, or independent]) codes with the number assigned to each subject.

Figure 7. Multiclass confusion matrix for training, validation, and test, respectively. Each matrix showcases the count of accurately predicted patients,
contrasting with those whose predicted labels differ from the true labels.

Discussion
The use of mobility recording devices identifies different
mobility patterns and relates them to functional capacity inpatients with polypathology. One of this study’s findings ishttps://formative.jmir.org/2024/1/e52344

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improving functional capacity measured by the BI in patients
after using the mobility devices for 6 months in a realenvironment. This improvement is slight, 13 points in the caseof moderate dependence and 6 points in mild dependence. We
believe that it reflects an effect of the empowerment experienced
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by patients with the use of mobility devices. Therefore, the useof such mobility monitoring devices may have a potential impact
on the management of complex chronic patients and could be
included as part of clinical follow-up practices. Specifically,
during the study execution in a real environment, patient
empowerment was related to patient participation indecision-making, gaining control, and learning about theirhealth.
In addition, patients’ sense of empowerment was related to lessfrustration with the technology [29]. This effect is well-known
in the literature. A systematic review of 71 articles analyzing
patients’ expectations of digital tools found that mobile apps
increase patient engagement and motivation, especially when
they can visualize parameters graphically and thus monitor theiroutcomes over time [30]. The evidence is scarce in patients with
polypathology; but in another study of our group, we foundsimilar results with slight improvements in functional capacity[31]. This empowerment is going to help patients in the
self-management of their diseases. Health status has shown thatincorporating digital technology into patients’ lives increases
their awareness of lifestyle behaviors, which has helped themunderstand how to manage their health better and promoteautonomy [32]. Longer term studies are needed to confirm thisbenefit, although it could be an alternative to integrate into theclinical practice of these patients to minimize their functional
impairment.
Another possible beneficial effect of continuous monitoring of
the functional capacity of patients with complex chronic diseases
is the early detection of functional deterioration that may be thebeginning of exacerbations of their diseases. If these data areintegrated into the health care computer system, alarm situations
could be determined that would allow early reaction by healthstaff to treat such exacerbation, prevent its progression, andminimize the functional deterioration that could be caused tothe patient.
It should be noted that commercially available mobility
monitoring devices have been used for this study and devices
specifically designed for the study were not required. This favors
cost reduction when considering the implementation of activity
monitoring in patients with polypathology in real-world settings.
Since the population with mobility devices is growing, with
515 million units sold in 2022, and patients with polypathology
are a population that continue to increase and probably havetheir own mobility measurement device [33], the costs are thus
reduced by integrating data in the informatics systems of thedifferent health care organizations.
Another contribution of this study was to determine that mobility
devices do not accurately recognize patients’ steps when using
walking aids. For that reason, and to avoid this possible bias,
patients were categorized into 3 groups depending on thewalking aids. Additionally, caregivers assisting patients duringphysical activity have been classified similarly to canes orwalkers due to their similarity in providing walking support.
After the inclusion of an extra variable with the group to which
the patient belongs, the ML model has managed to alleviatethese limitations, achieving a good performance.

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Alvarez-Romero et al
Concerning the data for the ML model, the use of the maximum
value of steps taken in each of the 4 intervals defined(morning-afternoon-evening-overnight) yielded the mostpromising outcomes. This finding can be attributed to the limited
and typically short-lived movements observed in patients withcomplex chronic conditions, which rarely extend beyond anhour. By leveraging the maximum step count within each time
segment, we effectively capture the most significant andrepresentative activity level during that period, thus optimizing
the model’s performance. The selected intervals concur withthe typical Spanish timetable for meals. Furthermore, the mean
and SD data values found in the training and validation sets
were similar, suggesting consistent levels of variability in bothdata sets. Moreover, including the heart rate information,measured by the WAT, and its relationship with the step countis proposed as a future study. Therefore, it could help todistinguish the requirement of the physical activity considering
the cross-correlation or the cosine similarity between the step
count and the heart rate.
Partitioning the data set into 7 equally sized subsets, with eachfold serving as a validation set while the remaining folds areused for training, ensures robustness in selecting the optimal
settings that yield the best predictive accuracy for the BI [34].
The hyperparameters play a crucial role in controlling thecomplexity and generalization of the decision tree model. Bytuning these hyperparameters, the cross-validation process aims
to find the optimal combination that balances model complexityand performance, resulting in a decision tree model withimproved predictive capabilities [28].
Using a decision tree as a regression model holds paramountimportance in the biomedicine field, particularly due to thenecessity of using a highly interpretable model that can beeffectively used and comprehended by the medical team [35,36].
The interpretability of the model enables medical professionals
to understand the underlying decision-making process and gain
insights into the factors influencing the predictions. Thistransparency fosters trust and facilitates collaboration between
the model and the clinicians. Although more complex
approaches exist, such as random forest or extreme gradientboosting, the ability to provide better results than decision trees
in terms of accuracy most of the time, their lack ofinterpretability, and the limited sample size of this study advise
against its use. Under these circumstances, a valid alternative
to regression trees is multiple linear regression. However, alinear regression model based on the same variables as thedecision tree has been performed, yielded inferior results.
Regarding the model performance, upon comparing the results
obtained from the model’s predictions with the ground truth,
the decision tree model generates accurate predictions.
Therefore, the decision tree model can assess the functional
capacity of patients based on data collected from the WAT. As
observed, the errors remain similar among the training,validation, and test sets. Hence, this confirms that the model
can generalize to unseen cases.
There is an imbalanced distribution of classes in the
production-like test set, as shown in Figure 7. This discrepancy
arises from the natural transition of patients between classes B
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JMIR FORMATIVE RESEARCH
to C and A, due to changes in the BI, coupled with the criterionof minimum data required for data set inclusion. It is noteworthy
that the test set could have been randomized to achieve an even
distribution of class numbers, akin to the training and validation
sets. However, given the primary goal of evaluating the model’s
performance in a realistic production setting, this randomization
was deliberately omitted.
In addition, it is worth noting that the MAD is the most suitableperformance measure in this case. This choice is justified by
the variability in step measurements captured by the WAT andthe relatively small data set. It is possible that a patient withinaccurately measured data could significantly influence theerror measure, particularly in terms of absolute or squared errors.
By considering the MAD as the primary metric, we mitigate
the impact of outliers or measurement inconsistencies, ensuringa more robust evaluation of the model’s performance inpredicting the BI.
On the other hand, when considering the performance obtainedin the classification problem, the accuracy remains consistentin training, validation, and test sets. The main objective ofperforming regression followed by classification into classes
A, B, and C is due to the continuous nature of the BI variable.
When aggregated into these 3 intervals, estimating BI solely
through classification becomes complex, as small differencesin BI values may result in a class label change. Therefore,
regression allows the model to capture the underlying continuous
relationship within the BI data, enhancing its ability to makeAlvarez-Romero et al
more accurate and robust predictions while assigning theappropriate class labels based on the predicted values.
Furthermore, the model does not need to include clinicalinformation such as specific disease, number of comorbidities,
severity of disease, and so forth. Therefore, it can be regarded
as a general model for patients with complex chronic diseases
without specific clinical data, facilitating the development of amethodology that allows estimating the BI at any given momentusing the information collected by the WAT over the last 30
days.
This study has several limitations. Since this was a pilot study
with a small number of patients, the results should be confirmedby studies with a larger population. Prospective studies areneeded to analyze whether identifying mobility changes andtheir transfer to health care systems can have care implications
and improve the health status of patients with multiple
pathologies. A notable element is that the bracelet does notregister well the physical activity of patients who use a cane or
crutches (or a wheelchair) since it cannot measure steps. A
priori, this could be a limitation of the study. Still, adjusting themodel by identifying walking aid devices and evaluating otherparameters makes it possible to identify and predict mobility
patterns in these patients.
In conclusion, using commercially available WATs makes it
possible to identify different mobility patterns and relate themto functional capacity in patients with polypathology according
to the BI without using clinical parameters.

Acknowledgments
This work was supported by the chronic-IoT project (Agencia Estatal de Investigación, PID2019110747RB-C21/AEI/10.13039/501100011033), which has received funding from the Ministry of Science, Innovation andUniversities of the Government of Spain and the State Research Agency. Also, this research has been cosupported by the Carlos
III National Institute of Health, through the IMPaCT-Data program (code IMP/00019), and through the Platform for Dynamization
and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector(code PT20/00088), both co-funded by European Regional Development Fund (FEDER) “A way of making Europe.”

Data Availability
The data sets generated during and analyzed during this study are not publicly available because there is no Ethics Committee
approval for this purpose, since the received Ethics Committee’s approval was related to carrying out this study. However, they
could be available from the corresponding author on reasonable request and this authorization shall then be requested.

Conflicts of Interest
None declared.

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Abbreviations
BI: Barthel Index
IoT: internet of things
MAD: median absolute error
ML: machine learning
WAT: wearable activity tracker

Edited by A Mavragani; submitted 18.09.23; peer-reviewed by S Okita; comments to author 03.01.24; revised version received
18.01.24; accepted 19.02.24; published 19.04.24
Please cite as:
Alvarez-Romero C, Polo-Molina A, Sánchez-Úbeda EF, Jimenez-De-Juan C, Cuadri-Benitez MP, Rivas-Gonzalez JA, Portela J,
Palacios R, Rodriguez-Morcillo C, Muñoz A, Parra-Calderon CL, Nieto-Martin MD, Ollero-Baturone M, Hernández-Quiles C
Machine Learning–Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot
Prospective Single-Center Observational StudyJMIR Form Res 2024;8:e52344
URL: https://formative.jmir.org/2024/1/e52344
doi: 10.2196/52344
PMID:©Celia Alvarez-Romero, Alejandro Polo-Molina, Eugenio Francisco Sánchez-Úbeda, Carlos Jimenez-De-Juan, Maria Pastora
Cuadri-Benitez, Jose Antonio Rivas-Gonzalez, Jose Portela, Rafael Palacios, Carlos Rodriguez-Morcillo, Antonio Muñoz, Carlos
Luis Parra-Calderon, Maria Dolores Nieto-Martin, Manuel Ollero-Baturone, Carlos Hernández-Quiles. Originally published in
JMIR Formative Research (https://formative.jmir.org), 19.04.2024. This is an open-access article distributed under the terms of
the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly
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