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 the coordinated use of various care levels and, in some cases, health and 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 or more chronic diseases coexist. It is a population characterized by frailty, polymedication, old age, hyperfrequent use of emergency services, and frequent re-entering. It is estimated that 70% to 95% of the older people in our environment have 1.2 to 4.2 chronic diseases, which constitute the leading death cause 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 more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic 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 as mortality, hospital admission, and stay length in rehabilitation departments. In addition, it is an indicator to assess the functional and prognostic capacities of patients with complex chronic diseases [8,9]. The BI is a simple measure developed on 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 and the bed, moving, going up and down stairs, or showering). The total score can vary between 0 (fully dependent) and 100 points (completely independent) [10].

Concerning functional capacity, physical inactivity is defined as 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 can even lead to various diseases [11]. In addition, physical inactivity itself decreases the physical fitness of the person, the duration of good health, and the age of onset of his or her first chronic illness. Relative to this, there are several parameters to assess the physical inactivity of the person, such as the number of 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 been studied. Various studies used accelerometers on the thigh to control 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 widely distributed throughout the population. These devices provide information on the paths, the number of steps, the speed of the march, 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 been developed to collect vital signs from patients. This system is intended 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 of stay 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 the development of more hardware devices to enhance health care delivery and turn the concept of “a doctor in your pocket” into a reality for patients.

We would like to emphasize that using sensors to obtain health information currently has a specific trajectory [17-19]. Mobility has long presented prominent importance when dealing with diseases whose onset and symptomatic progression affect the functional capacity of the subject [20]. Recently, machine learning (ML) techniques are increasingly being considered to characterize the movement, or some particularities of the movement, which can provide relevant information about the patient’s clinical status [21,22]. Some works investigate the relationships between movement and specific clinical pathologies [23,24].

Despite these initiatives, the evaluation of the mobility of patients with complex chronic diseases and their relationship with 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 based on artificial intelligence and the internet of things (IoT) environment, aiming to predict changes in the clinical condition of patients with complex chronic diseases through the prediction of the BI to know the clinical and functional status of the patients.

The use of mobility recording devices identifies different mobility patterns and relates them to functional capacity in patients with polypathology. One of this study’s findings is improving functional capacity measured by the BI in patients after using the mobility devices for 6 months in a real environment. This improvement is slight, 13 points in the case of moderate dependence and 6 points in mild dependence. We believe that it reflects an effect of the empowerment experienced by patients with the use of mobility devices. Therefore, the use of 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 in decision-making, gaining control, and learning about their health.

In addition, patients’ sense of empowerment was related to less frustration 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 their outcomes over time [30]. The evidence is scarce in patients with polypathology; but in another study of our group, we found similar 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 that incorporating digital technology into patients’ lives increases their awareness of lifestyle behaviors, which has helped them understand how to manage their health better and promote autonomy [32]. Longer term studies are needed to confirm this benefit, although it could be an alternative to integrate into the clinical 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 the beginning of exacerbations of their diseases. If these data are integrated into the health care computer system, alarm situations could be determined that would allow early reaction by health staff to treat such exacerbation, prevent its progression, and minimize the functional deterioration that could be caused to the 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 have their own mobility measurement device [33], the costs are thus reduced by integrating data in the informatics systems of the different 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 the walking aids. Additionally, caregivers assisting patients during physical activity have been classified similarly to canes or walkers 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 alleviate these limitations, achieving a good performance.

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 most promising outcomes. This finding can be attributed to the limited and typically short-lived movements observed in patients with complex chronic conditions, which rarely extend beyond an hour. By leveraging the maximum step count within each time segment, we effectively capture the most significant and representative activity level during that period, thus optimizing the model’s performance. The selected intervals concur with the 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 both data sets. Moreover, including the heart rate information, measured by the WAT, and its relationship with the step count is proposed as a future study. Therefore, it could help to distinguish 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 each fold serving as a validation set while the remaining folds are used 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 the complexity and generalization of the decision tree model. By tuning these hyperparameters, the cross-validation process aims to find the optimal combination that balances model complexity and performance, resulting in a decision tree model with improved predictive capabilities [28].

Using a decision tree as a regression model holds paramount importance in the biomedicine field, particularly due to the necessity of using a highly interpretable model that can be effectively 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. This transparency fosters trust and facilitates collaboration between the model and the clinicians. Although more complex approaches exist, such as random forest or extreme gradient boosting, the ability to provide better results than decision trees in terms of accuracy most of the time, their lack of interpretability, 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, a linear regression model based on the same variables as the decision 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 to C and A, due to changes in the BI, coupled with the criterion of 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 suitable performance measure in this case. This choice is justified by the variability in step measurements captured by the WAT and the relatively small data set. It is possible that a patient with inaccurately measured data could significantly influence the error 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, ensuring a more robust evaluation of the model’s performance in predicting the BI.

On the other hand, when considering the performance obtained in the classification problem, the accuracy remains consistent in training, validation, and test sets. The main objective of performing 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 differences in 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 make more accurate and robust predictions while assigning the appropriate class labels based on the predicted values.

Furthermore, the model does not need to include clinical information 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 a methodology that allows estimating the BI at any given moment using 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 confirmed by studies with a larger population. Prospective studies are needed to analyze whether identifying mobility changes and their 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 not register 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 the model by identifying walking aid devices and evaluating other parameters 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 them to functional capacity in patients with polypathology according to the BI without using clinical parameters.