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Francia, P. Diabetic Foot with Exercise Therapy. Encyclopedia. Available online: https://encyclopedia.pub/entry/17688 (accessed on 10 December 2023).
Francia P. Diabetic Foot with Exercise Therapy. Encyclopedia. Available at: https://encyclopedia.pub/entry/17688. Accessed December 10, 2023.
Francia, Piergiorgio. "Diabetic Foot with Exercise Therapy" Encyclopedia, https://encyclopedia.pub/entry/17688 (accessed December 10, 2023).
Francia, P.(2021, December 31). Diabetic Foot with Exercise Therapy. In Encyclopedia. https://encyclopedia.pub/entry/17688
Francia, Piergiorgio. "Diabetic Foot with Exercise Therapy." Encyclopedia. Web. 31 December, 2021.
Diabetic Foot with Exercise Therapy
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Diabetic foot (DF) is a long-term diabetes complication that can increase morbidity and mortality in addition to affecting mobility and the overall well-being of patients. In particular, the DF has a complex multifactorial pathogenesis that makes it difficult to prevent and treat. In this sense, it is well known that the prevention and treatment of DF disease requires a multidisciplinary approach. Physical activity has always been considered a potential pillar in the prevention of DFD. More recently, it has been reported, that physical activity can contribute in the wound healing phase. Unfortunately, to date, there is no clear and definitive evidence on the role that protocols of physical activity can play in the treatment of patients at risk or with DFD. In order to pursue this objective, it is important to standardize exercise training protocols for the prevention or treatment of these patients. Moreover, it is now possible to organize innovative methods of conducting, monitoring and analysing physical activity performed by patients, even remotely.

diabetes foot ulcer physical activity exercise technologies proper execution

1. Introduction

Diabetes mellitus is a group of chronic metabolic diseases affecting an increasing number of patients worldwide and which, over time, can favor the development of particularly feared complications such as those affecting the foot.Diabetes is rising and will continue to grow around the world. It has been estimated that over 460 million people worldwide suffer from diabetes [1][2]. Diabetic foot is the major long-term diabetes complication that can increase morbidity and mortality in addition to affecting mobility and overall well-being of patients with both type 1 and type 2 diabetes mellitus [3][4]. The annual incidence and prevalence of DF is around 2% and 6% respectively while up to 25% of patients with some form of peripheral neuropathy may have experience of foot ulcer [2][5][6][7].

These epidemiological data show that DF represents a serious health problem in many countries of the world which need to be addressed [2][3][4]. As regards the specific relationship between physical activity (PA) and DF, it is known that movement is at the same time a column of prevention and an important risk factor for DF [8][9][10][11]. In this sense, it is known that, if on the one hand PA is necessary to obtain appropriate metabolic control and physical condition, on the other hand, it can lead to a mechanical stress that favours the onset of diabetic foot [2][12][13][14][15].

Over the last few years, an increasing number of devices for monitoring patients with diabetes and solutions for data transmission, storage and processing, offer new opportunities in managing the relationship between PA and DF [16][17][18][19]. This technological evolution is strongly pointing towards a new way of managing the lifestyle of patients at risk of, or with diabetic foot (R-DF) [9][20][21][22]. The data relating to numerous physiological parameters of each patient can now be collected and integrated with other useful information so that they can be studied through the application of increasingly powerful data analysis systems [16][23][24].

A key role in enabling this evolution in information management was played by the development of sensors [17][25][26][27].The extension of the internet to the daily life of patients has enabled the creation of more and more devices capable of acquiring environmental information and keeping it in communication with the data storage and processing units [18][28][29]. With regard to PA, for several years the progress of sensors has been such as to allow the inversion of the same protocols based on the use of outpatient tests and questionnaires, which have always been adopted, for the assessment of lifestyle and motor condition of the patient with diabetes [9][30][31][32].

Even in the case of patients at R-DF, the monitoring of PA carried out can be performed before the specialist consultation and represents the starting point of any outpatient and/or laboratory tests considered useful to confirm or clarify what has been observed and to give indications on the lifestyle to be followed. Today, many assessments that previously were not possible can be carried out by monitoring patients during their usual activities of daily life and they can provide indications that cannot be obtained on an outpatient basis [9].

The increasing use of mobile phones, computers, tablets and other electronic tools that can be added to the classic means of communication (ie telephone, fax, mail), offers further opportunities for communication, knowledge and management of patients at R-DF themselves [10][25][33][34]. It is conceivable that, by the monitoring and continuous management of lifestyle, it is finally possible to use PA in the prevention of DF and in the treatment of patients at R-DF, overcoming limits, uncertainties and fears that are still widespread today

2. Exercise Training and Diabetic Foot

Multiple factors should be considered in order to properly manage the daily PA of patients with diabetes at R-DF. In particular, in the case of subjects with diabetic peripheral neuropathy, the reduced sensitivity of the foot can lower or eliminate the ability to sense harmful events such as excessive plantar pressure and stress that can be caused by walking [3][4][11][17][35]. In these patients, the presence of other problems such as peripheral vasculopathy can further increase the ulcerative risk and negatively affect the daily PA carried out causing pain at rest, claudication and a reduction in walking speed and distance. In turn, peripheral diabetic neuropathy can lead to the development of feared ulcerative risk factors such as alterations of balance, posture, muscle strength and joint mobility, all factors that can negatively affect the biomechanics of gait and in particular of foot rollover [3][8][36][37]. These factors, in addition to the presence of problems that can contribute to the occurrence of ulcers as a result of walking, such as deformities or calluses of the foot, should be promptly identified before intervening on the lifestyle of patients at R-DF [8][36][38][39].

Considering the complexity of the condition of patient's at R-DF, in order to give indications or propose appropriate protocols of PA, it may be important to distinguish between patients not at risk or low, medium or high risk and patients with ulcer and so it is important to evaluate significant parameters of the PA planned or carried out such as: type, intensity, duration and distribution (TIDD) [4][14][40][41].

Regarding the assessment of the level of ulcerative risk associated with walking some of the important parameters to be considered and monitored are: the peak of the pressures exerted on the foot and value of the cumulative plantar pressure in addition to the distribution of stress at the foot level in the 24 hours and/or in the course of different periods [10][12][35][40][41]. In addition to all this, when it is not possible to make a direct evaluation of the plantar pressures it may be important to know and monitor some parameters relating to the gait such as: number of steps in given time intervals, step length and support base width. These parameters can be useful in assessing the quality of walking as well as being able to give an indirect indication of the amount of plantar pressure and other characteristics [27][33][42][43][44]. Furthermore, the amount of time spent in an orthostatic position, which is usually more than that spent in walking, can be an important source of stress for the foot and should be taken into consideration [8][12][44].

Other structural or occasional factors such as the characteristics of the footwear or the terrain (outside-inside the home) may have a role in the distribution and amount of forces exerted at the foot level and therefore on the ulcerative risk. The number and complexity through which the various ulcerative risk factors or others positive factors (i.e. walking canes, crutches and trakking poles) can make some activities of daily life such as walking more or less dangerous, help to define the dimension of the challenge to be faced in defining PA protocols for fragile patients such as those at R-DF [41][45][46].

If in the patient with diabetes not at risk it is important to practice PA aimed at achieving the appropriate metabolic control considering the personal needs and interests, in the case of patients at medium-low ulcerative risk the presence of neuropathy and / or peripheral vasculopathy can be a difficult hurdle to overcome [4][9][47][48].

For these patients as well as for those at high risk, some studies have reported how the amount of daily PA performed in the period preceding an ulcerative event may be reduced and /or can vary more than in subjects who will not develop a foot ulcer [10][49]. This pattern of daily PA seems to contradict the rule that walking-related plantar pressures is a major risk factor. Although it has been suggested that managing the variability of the daily PA of these patients could reduce the risk for ulceration too, the reason for the onset of the foot ulcer seems to be more due to the fragile condition of these patients [9][10][11][49].

Even more evidently, the PA performed by patients with ulcer is usually severely limited. In these patients, reducing high foot pressure also through the use of offloading devices is one of the main goals in healing and preventing foot ulceration [4][10][12]. At the same time, another important challenge in the management of patients at R-DF or with ongoing ulcer is to avoid over-protection of the foot and define the moment when the patient can begin to put it in the orthostatic position or walking. Promptly pursuing this objective could avoid compromising more than necessary a lot of parameters such as muscle strength, joint mobility, balance and walking quality as well as vascularization and sensitivity, that are already strongly compromised in many patients [10][11][49][50][51]. The goal of these patients should be to perform a level of PA necessary to restore or maintain the good condition of the lower limbs, metabolic control and management of possible other co-morbidities, avoiding peaks of activity, inactivity and the recurrence of ulcer [8][9][49].

3. Monitoring and Evaluation of Proper Execution of Exercise

The possibility of having a wide range of sensors and devices has enabled even more to objectively evaluate numerous important parameters, thus overcoming some limitations of the past and implementing the use of PA in the treatment of patients at R-DF [11][17][27][43]. These devices are always more complex, they can be wireless, and used both for evaluating parameters relating to movement and for the transmission of data both in real time and remotely [25][28][33]. In this way, the set of devices used in monitoring patients with diabetes contributes to forming the world of the Internet of Things (IoT) [18][25][52].

With regards to plantar pressure, sensors can be used to build platforms and study foot support in orthostatic and dynamic conditions, in the outpatient and laboratory settings or as wearable devices [36][53][54]. Already in the early 60s of the last century devices equipped with a limited number of independent pressure sensors were used because they were wearable and allowed measuring the pressures exerted at specific points on the foot. In fact, with these tools it is possible to freely put the sensors in contact with the skin or on the sock choosing the point or insert them into pre-built structures such as insoles [17][22][42][55][56].

Compared to devices with pressure sensors inserted in predisposed structures, those equipped with freely positionable sensors enable to better meet the needs of the individual patient. Unfortunately, the use of devices with position able sensors has revealed limitations such as longer test preparation and conducting times, possible limits on the repeatability of measurements as well as the difficulty of carrying out multi-parametric or long-term monitoring [17][43][56]. Even devices such as insoles built to contain a limited number of pressure sensors in standardized points, show limits with respect to the possibility of adapting to different footwear and sizes, thus hindering the possibility of wearing them throughout the whole day [17][57][58]. Over the years, the use of wearable devices has encountered a slow but growing interest because, unlike platforms, they allow you to directly assess the patient during activities of daily life.

To date, in the management of patient at R-DF outside the laboratory setting, the size and encumbrance created by the various components such as connected cables, acquisition box and battery, limits the use of wearable devices and the possibility of long term monitoring (i.e. 24/7) [56][57][58].

In order to overcome these limits, in recent years some less bulky devices, equipped with wireless transmission and associated with applications for mobile phones and other electronic devices have been created [29][33][59][60]. In monitoring patients with diabetes even the use of the smart socks allows the limits linked to the use of insole, to be overcome. In this sense, increasing research activity in the field of diabetic foot has been addressed to the study of smart socks [17][22][27]. In particular, socks that can be adapted to different patients, wireless and capable of monitoring pressure and other parameters useful in the patient’s management, including movement and temperature have been proposed [22][23][42]. As in the case of freely positionable sensors, also for smart socks, it is necessary to check that the devices do not involve a risk of injury.

Motion sensors are also considered very important and have been widely used in the study and care of patients with diabetes [9][26][49][56]. Some centuries have passed since the first rudimentary pedometers were proposed but only in recent years has the development and use of such devices increased greatly [61]. As for patients at R-DF, more and more complex step counters that can be worn in different parts of the body (neck, wrist, waist, thigh, ankle, foot) and during different activities: (walking, running, cycling, swimming) have been made available and used on a large scale [31][47][61][62]. While some devices marketed could have significant margins of error in defining the number of steps recorded, other recent and advanced devices, sometimes also present in mobile phones themselves, allow to reliably measure the number of steps taken and many other parameters including: distance travelled, speed maintained, time and duration of activities [61][63].

Thanks to the use of motion sensors it is possible to obtain information relating to the assumed posture (lying, sitting, orthostatic, dynamic), the duration and distribution of the activity performed [20][26][42][64]. Some devices have been designed to show the intensity of the activity and therefore the relationship between light, moderate or vigorous, this is another parameter which is considered important in the management of patients at R-DF [12][40]. These sensors could be used to monitor the variability of the daily PA, highlighting a possible forthcoming risk of ulcer [27][29][60]. As for the foot pressure, it can be difficult to monitor the PA performed over 24 hours. A possible solution is to use a digital diary accessible also remotely in which patients can report activities not monitored by sensors [31][41][65].

A limitation due to the use of these devices is that the differences in results may depend on the type of instrument used. This suggests the importance of adopting suitable tools and maintaining them over time. Regarding the choice of devices, there are considerable differences between the various products on the market. Some of the characteristics for which the devices may differ are: number of sensors used, type of sensor (capacitive, resistive, piezoelectric), evaluation range and frequency, information storage and communication system and power supply system. It is also important to have, when possible, wireless instruments that can be connected to the internet with suitable sensitivity, repeatability, hysteresis and stability that can be integrated into multi-parameter monitoring systems [27][28][56][60].

Regarding pressure sensors, the transition from a single axis to a multiaxial load measurement of forces, represented an important step forward allowing better studying of the gait, foot rollover, and hence intervening with by structured and unstructured PA [17][23][33][42]. Even with regards to accelerometers, the transition from the possibility of acquiring information on a single axis to the ability of moving forward the accelerometers on the three axis, sometimes associating the information collected by magnetometers and gyroscopes, represents an important step forward in the analysis of PA [26][33][43][66].

Other sensors in addition to those aimed at measuring movement and its effects can be used for remote monitoring and provide important information [28][56].  Image acquisition plays another important role for the treatment of patients at R-DF., The availability of appropriately acquired images can allow the checking of patients' condition, reproduce the same foot or part of it through the use of a 3D printer or hologragraphic projectors. Videos could also be used for a summary evaluation of the gait [29][33][34][67]. Sensors detecting temperature, foot humidity and galvanic skin response have been used in the monitoring of patients at R-DF. In particular, many studies have been aimed at evaluating the temperature of the feet or joint mobility [24][29][33][34][56]. As in part proposed by many studies, in order to ensure better management of the patients' lifestyle, devices used for monitoring parameters directly affected by movement can work alongside those for long-term monitoring physiological parameters such as glycaemia and insulin therapy or variables like climate and environmental conditions (i.e. geolocation) [16][33][49][56].

  • Techniques data analysis: from data to knowledge

In order to make the best use of the data relating to the multi-parametric monitoring of patients at R-DF, the considerable volume of data collected thanks to the use of the sensors must be transmitted to the analysis units or specific memory support. In this sense, it is known that the 5G mobile network can support thousands and thousands of connections thus enabling the various diabetes units to carry out multi-parameter monitoring of patients [68][69][70]. The management of such data using appropriate analysis tools is currently a goal of great importance for the healthcare system [33]. The data on patients’ monitoring may include important information which cannot be highlighted by traditional manual data analysis but which can instead be contained using appropriate data analysis techniques. The ability to work on this information potential would allow, if correctly understood, better decisions to be made for patients’ care as well as reducing human error.

A discipline that deals with obtaining information by studying the possible relationships existing between the data in the archives is Data Science (DS) [71][72]. The DS aiming at extracting knowledge from data through the use of different systems including Data Mining (DM), Artificial Intelligence (AI) and therefore Machine Learning and Deep Learning [16][24][68][73][74].

Data Mining (DM) is a learning strategy of an inductive nature and is the core of Knowledge Discovery in Databases, that is, the overall process of seeking knowledge from data stored in large databases [16][73]. The goal of DM is therefore to extract knowledge by identifying valid, useful and not known a priori patterns. In this way, DM is a phase of the process of knowledge extraction and arises as an activity of selection, exploration and modelling of large masses of data in order to highlight relationships not known a priori between variables (or features) and to identify patterns hidden in the data [73][74]. The models obtained by methods of DM provide a conceptual generalization of the input data with the aim of being able to predict the value of an output attribute. It is therefore a process that employs one or more computerized learning techniques to automatically analyze and extract knowledge from the data stored in a database allowing the enhancement of the same predictive capacity of the data used in order to indicate possible new perspectives in patient management [58][73].

Even the use of AI in the treatment of patients at R-DF represents a great opportunity to overcome some limitations that have always hindered the use of PA in the management of these patients. AI aims to create "intelligent" systems, that are systems capable of giving performances that for an external observer may appear in line with human behaviour [16].

In association with Data Mining and AI techniques, systems such as Machine Learning (ML), Deep Learning (DL) or Artificial Neural Network were used for the smart continuous monitoring of patients with diabetic foot [16][24][33][58][75]. The aim of these studies was to design, implement and test methods for the prediction of DF and then train algorithms to recognize if a subject was healthy or diabetic with or without neuropathy [58][75][76].

It has recently been proposed how foot pressure mappings collected during testing can be used for the detection of foot ulceration. In these studies, in order to classify patients, Data Mining and Machine Learning or Artificial neural network analysis were adopted [33][74][75]. In Machine Learning, the algorithms used to analyze the data also consider any new relationships identified. In this case, the machine tries to learn independently from the available data in order to improve its classification algorithms and its performance. All this has among the main objectives that of predicting possible future events [33][58].

As regards the monitoring of patients with diabetes using ML algorithms, some works have proposed systems based on the use of smart devices such as smartphones and portable sensors for the evaluation of parameters regarding the diabetic patients: PA, blood sugar level, temperature of the feet. A smartphone transmitted the data collected by the sensors to a database station through the 5G mobile network. Subsequently the intelligent system collected the data received and performed data classification demonstrating promise in accuracy and reliability [33][68].

  • System Output: information and instruction

Both from the monitoring of patients at R-DF and from the processing of the data collected, it is possible to obtain information that can be communicated to the same patient, to the healthcare professional, and to other people or structures involved in the management of the relationship between PA and patient in R-DF.

The search for an appropriate management of these outputs can be of great importance considering both the possible emergency situation and the complexity of the clinical picture that patients at R-DF can show [29][47][60][65]. Over the last few years, some studies have verified the usefulness of scheduled telephone calls and telephone messages in the prevention and treatment of ulcers. Social media have also been widely used to communicate with patients, including through mobile phones and virtual consultation [33][34][77][78][79]. These outgoing communication methods have the advantage of interpersonal contact and do not require special tools or skills. Among the limits of the type of communication, there is the difficulty in managing and storing the information collected, communicating large amounts of data even graphically and difficulties in modifying or intensifying the communication in the event of risk of ulcer.

More recently, the use of applications with appropriate user interfaces has allowed some of these problems to be overcome. Applications that can be specifically created for the management of patients at R-DF and, in addition to outgoing messages, can allow the acquisition of information directly from patients, from worn devices and from other applications in use [22][29][34][67]. The data collected in this way can be shown directly to patients or sent to analysis centers or specific memory media. The results of the processed data can be used to give indications or alarms. In this way, the applications that can be installed directly on mobile phones represent an intermediate station between the sensors and the processing center [22][47][60]. This intermediate station, in analogy with the nervous system, on the one hand can be seen as a passage that slows down the streaming of information, on the other hand, it allows a first limited analysis of the data. The data analysis carried out at the application level could be managed in such a way as to lead to the generation of autonomous and immediate "reflected" output responses (real-time decision), without affecting the central system which will only subsequently receive the information with respect to what success. The "reflex" responses generated in this way will timely and better respond to particular needs such as a forthcoming risk for the patient [34][60].

In addition to applications, some projects have involved the development of dedicated web sites managed by operators and specialists, where, at least in part, the information can be consulted by patients and a series of services can be activated. Sometimes these websites are adopted as the only means of output avoiding the use of applications for mobile phones or tablets [33][47].

With regard to physical activity in the case of patients at R-DF, each of the movement variables (type, intensity, duration, distribution) considered useful in the prevention of diabetic foot, in addition to the quality of walking, can be appropriately managed thanks to the use of output [9][14][22][28][42][41]. In particular, giving information about PA, plantar pressures (peaks, distribution, cumulative amount, distribution), walking (number of steps, distance covered, speed) maintained posture (lying, sitting, orthostatic, dynamic) can be important for the prevention of ulcers [33][42][59][66]. Even variations considered significant in other potentially monitored parameters such as foot temperature or simply related to glycaemia can determine the content of the outputs aimed at managing the PA. More generally, the outputs can be modulated on the basis of the ulcerative risk that emerges from the patient's clinical history and for the pursuit of any other pre-established objectives (i.e. metabolic control, level of daily PA, plantar pressures) [29][60].

  • CLOSED-LOOP SYSTEM FOR PHYSICAL ACTIVITY MANAGEMENT

In recent years, the achievements of technology in the field of multi-parameter monitoring of PA in addition to data transfer and processing enabled the design or organization circular-reverberant, multi-station systems that can be partly autonomous [18][28][42][56]. In particular, the relationship between Artificial Intelligence, Data Mining and the Internet of Medical Things has consolidated the transition into a new phase of patient at R-DF management [18][28][68]. These systems, partly mimicking the nervous system, are able to collect, perceive, store and process information from the external environment (i.e. foot, patient and environment) and information already present. This information generates a continuous streaming towards the processing units and can also be processed through Artificial Intelligence systems and subsystems such as Machine Learning in order to generate outputs deemed useful for patient management [16][33][73].

The set of results can allow organizing outputs within a Telehealth system aimed at managing the patient at R-DF. These outputs may involve changes in the environment or in the behaviour of the patients themselves, changes that generate feedback for the system and power a reverberant and intelligent system [80][81]. A desired development of this system would be the possibility of managing devices dedicated to the prevention of DF on the basis of the information available. Some insoles, recently proposed, through the use of small electronic devices aimed to modulate the pressures exerted at the plantar surface of the foot in an increasingly effective and efficient way that can reduce the ulcerative risk [80].

It would also be interesting to develop Smart health-care systems addressed at patients at R-DF that foresee the PA and the behaviour that the patient will hold in the following hours and also give indications or generate outputs aimed at allowing the execution of activities by maximizing positive returns and reducing negative ones such as foot ulcer [66].

4. Discussion 

What we are experiencing is a transition period characterized by strong technological evolution which deeply revolutionizing health systems in addition to societies. The new phase in which we have been entering for several years has probably been accelerated as a result of the pandemic linked to Covid-19 and is characterized by the strong digitalization of services, the spread of telemedicine and smart health [29][79][82]. In this context, the management of the relationship between subjects at R-DF and PA could also undergo increasingly important changes with a possible growing use of adapted PA.

To date, many of the studies aimed at verifying the effects of PA on the condition of patients at R-DF have evaluated the effects of some supervised PA programs [30][37][83][84][. Since it may be considered dangerous to give PA indications to fragile people such as patients at R-DF without constantly monitoring any changes in the health condition and the in same PA performed, the presence of a therapist and suitable environments provides a guarantee [8][84][83]. Unfortunately, patients at R-DF may encounter several types of difficulties (i.e. health conditions, long journeys, time constraints) in participating constantly and for years in structured activities. Furthermore, any positive effects that may be achieved with these interventions are usually lost quickly once the training is stopped. All these limits have not yet allowed the real use of PA in the management of patients at R-DF [8][9][30][85]. Compared to this apparent blind alley, the possibility of remotely monitoring a large number of parameters considered important in real time could finally allow the use of the PA in the prevention of DF [11][33].

As proposed in previous studies, a possible solution could be to alternate training sessions in the presence of the therapist with home ones monitored remotely. In this condition, also the use of virtual reality could be useful for the treatment of patients with diabetic peripheral neuropathy [86][87]. Such an organization would overcome the limits of telerehabilitation, in fact it can be important for therapists to have the opportunity to work in contact with patients in order to check the correct execution of the exercises and verify the suitability of the proposed training protocol and patients’ condition [8][81]. Multi-parameter monitoring can also be used to address the important issues related to the subjective timing of adaptation to lifestyle changes. In this sense, in addition to the intersubjective differences that patients may show, diabetes per se can be seen as a condition that tends to limit, constrain, stiffen many aspects of patients’ lives [28][88]. Consequently, even with regard to the PA, it could be important to avoid large and sudden changes in lifestyle.

Considering the individual patient, excessive or repeated variations in daily motor activity could be a source of injury or ulcer [9][10][11]. In this sense, even when the purpose of structured PA is the improvement of aerobic capacity or other parameters such as joint mobility or muscle strength, protocols should be organized over sufficiently long periods in order to avoid excessive stress for patients in particular at the foot level and more generally of the musculoskeletal system and metabolic control [4][11][12][28][32]. Such indications find further justification considering the same propensity to quickly lose the improvements obtained through structured PA that patients at R-DF can show [37][85]. In this sense, the search for sudden and evident variations in the training state mostly represents a risk for the patient himself [11][40]. For these patients, it seems more appropriate to set the goal of PA protocols to achieve a training condition that allows the carrying out of the activities of daily life correctly and safely [9].

The set of information that can be collected through patient monitoring would be useless if they were not properly studied by data analysis systems capable of extracting knowledge also in order to make predictions. The use of AI and Machine Learning systems could enable the prediction of lifestyle trends and movement, in order to organize corrective interventions, if necessary, for the prevention of the possible onset of ulcers of the foot [16][24][33][58][75].  Moreover, the multi-parameter monitoring, even if it cannot replace the functional assessments carried out in the outpatient setting, can allow to draw uninterrupted continuous lines describing the trend of the various parameters analysed.  The possibility of having these curves of a continuum can help manage the relationship between PA and patients at R-DF. In particular, the multi-parameter monitoring can allow the transition from a relationship with a discrete trend (risk level and PA protocol) to a continuous type of relationship (monitored patient condition and PA protocol). This could help improve understanding of the patient’s condition and better tailor the proposed treatment [33].

Regarding the duration of monitoring, in the next few years we will probably see the creation of an increasing number of systems for managing the lifestyle of patients at R-DF, and it is possible to hypothesize an increasingly early activation of such monitoring that could be started from diagnosis, in particular in the case of patients with type 2 diabetes [88][89].

This pervasive capacity of new technologies brings with it a series of doubts and risks [17][29]. In addition to the major problem linked to respect patients’ privacy, the possible loss of direct contact with patients, the risk of dissemination of the data collected are highly debated topics and in relation to which all necessary precautions must be taken. Organizing Diabetes Units capable of working using new technological resources and at least partial revision of patients’ management protocols represent an additional challenge. The type of patients’ collaboration in the use of new technologies could represent an important obstacle to the transition process (Tab. 1). From these assumptions, the need arises to be able to predict the potential and problems associated with the adoption of new technologies in the management of the relationship between PA and the risk of DF and trying to manage all this in the interest of patients [90][91][92].

Tab.1

5. Conclusions

The introduction of new technologies seems to have a profound effect on the evolution of the relationship between PA and patients at R-DF. Thanks to continuous multi-parametric monitoring, data analysis and knowledge extraction through Data Science systems, it is now possible to overcome previous limitations and finally think about a systematic use of PA in the management of patients at R-DF.  However, in order to take this important step forward, further important obstacles related in particular to data security and privacy must be overcome. Trying to maximize the possible positive effects related to PA avoiding the possible negative aspects associated with the strong digitalization  (Tab. 1)  represents at the same time an important challenge and an opportunity that should be seized in the interest of patients at risk of, or with diabetic foot.

References

  1. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet. 2021 Dec 19;396(10267):2019-2082. doi: 10.1016/S0140-6736(20)32374-6. Epub 2020 Nov 12. Erratum in: Lancet. 2021 Dec 19;396(10267):1978. PMID: 33189186.
  2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R; IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Diabetes Res Clin Pract. 2019 Nov;157:107843. doi: 10.1016/j.diabres.2019.107843. Epub 2019 Sep 10. PMID: 31518657.
  3. Bus SA, Lavery LA, Monteiro-Soares M, Rasmussen A, Raspovic A, Sacco ICN, van Netten JJ; International Working Group on the Diabetic Foot. Guidelines on the prevention of foot ulcers in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36 Suppl 1:e3269. doi: 10.1002/dmrr.3269. PMID: 32176451.
  4. Schaper NC, van Netten JJ, Apelqvist J, Bus SA, Hinchliffe RJ, Lipsky BA; IWGDF Editorial Board. Practical Guidelines on the prevention and management of diabetic foot disease (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36 Suppl 1:e3266. doi: 10.1002/dmrr.3266. PMID: 32176447.
  5. Diabetic foot facts: The global burden of diabetes and foot complications: the facts. Available from: https://d-foot.org/diabetic-foot/diabetic-foot-facts.
  6. Zhang P, Lu J, Jing Y, Tang S, Zhu D, Bi Y. Global epidemiology of diabetic foot ulceration: a systematic review and meta-analysis†. Ann Med. 2017;49(2):106-116. doi: 10.1080/07853890.2016.1231932. Epub 2016 Nov 3. PMID: 27585063.
  7. Armstrong DG, Boulton AJM, Bus SA. Diabetic Foot Ulcers and Their Recurrence. N Engl J Med. 2017 15;376(24):2367-2375. doi: 10.1056/NEJMra1615439. PMID: 28614678.
  8. Francia P, Gulisano M, Anichini R, Seghieri G. Diabetic foot and exercise therapy: step by step the role of rigid posture and biomechanics treatment. Curr Diabetes Rev. 2014;10(2):86-99. doi: 10.2174/1573399810666140507112536. PMID: 24807636.
  9. Francia P, Bellis A, Seghieri G, Tedeschi A, Iannone G, Anichini R, Gulisano M. Continuous Movement Monitoring of Daily Living Activities for Prevention of Diabetic Foot Ulcer: A Review of Literature. Int J Prev Med. 2019;10:22. doi: 10.4103/ijpvm.IJPVM_410_17. PMID: 30820309.
  10. Mueller MJ. Mobility advice to help prevent re-ulceration in diabetes. Diabetes Metab Res Rev. 2020;36(1):e3259. doi: 10.1002/dmrr.3259. Epub 2019 Dec 18. PMID: 31851432.
  11. Shah KM, Mueller MJ. Effect of selected exercises on in-shoe plantar pressures in people with diabetes and peripheral neuropathy. Foot (Edinb). 2012;22(3):130-4. doi: 10.1016/j.foot.2012.05.001. Epub 2012 Jun 6. PMID: 22677098.
  12. Bus SA. Priorities in offloading the diabetic foot. Diabetes Metab Res Rev. 2012;28(1):54-9. doi: 10.1002/dmrr.2240. PMID: 22271724.
  13. Amemiya A, Noguchi H, Oe M, Takehara K, Ohashi Y, Suzuki R, Yamauchi T, Kadowaki T, Sanada H, Mori T. Shear Stress-Normal Stress (Pressure) Ratio Decides Forming Callus in Patients with Diabetic Neuropathy. J Diabetes Res. 2016;2016:3157123. doi: 10.1155/2016/3157123. Epub 2016 Dec 5. PMID: 28050567.
  14. Colberg SR, Hernandez MJ, Shahzad F. Blood glucose responses to type, intensity, duration, and timing of exercise. Diabetes Care. 2013 Oct;36(10):e177. doi: 10.2337/dc13-0965. PMID: 24065851.
  15. Birke JA, Patout CA Jr, Foto JG. Factors associated with ulceration and amputation in the neuropathic foot. J Orthop Sports Phys Ther. 2000;30(2):91-7. doi: 10.2519/jospt.2000.30.2.91. PMID: 10693087.
  16. Rigla M, García-Sáez G, Pons B, Hernando ME. Artificial Intelligence Methodologies and Their Application to Diabetes. J Diabetes Sci Technol. 2018;12(2):303-310. doi: 10.1177/1932296817710475. Epub 2017 May 25. PMID: 28539087.
  17. Wang L, Jones D, Chapman GJ, Siddle HJ, Russell DA, Alazmani A, Culmer P. A Review of Wearable Sensor Systems to Monitor Plantar Loading in the Assessment of Diabetic Foot Ulcers. IEEE Trans Biomed Eng. 2020;67(7):1989-2004. doi: 10.1109/TBME.2019.2953630. Epub 2019 Dec 27. PMID: 31899409.
  18. AlShorman O, AlShorman B, Al-khassaweneh M, Alkahtani F. A review of internet of medical things (IoMT) - based remote health monitoring through wearable sensors: a case study for diabetic patients Indonesian Journal of Electrical Engineering and Computer Science. 2020;20(1):414~422 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v20.i1.pp414-422.
  19. Lung CW, Wu FL, Liao F, Pu F, Fan Y, Jan YK. Emerging technologies for the prevention and management of diabetic foot ulcers. J Tissue Viability. 2020;29(2):61-68. doi: 10.1016/j.jtv.2020.03.003. Epub 2020 Mar 17. PMID: 32197948.
  20. Drăgulinescu A, Drăgulinescu AM, Zincă G, Bucur D, Feieș V, Neagu DM. Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications. Sensors (Basel). 2020;20(15):4316. doi: 10.3390/s20154316. PMID: 32748872.
  21. Armstrong DG, Boulton AJ. Activity monitors: should we begin dosing activity as we dose a drug? J Am Podiatr Med Assoc. 2001;91(3):152-3. doi: 10.7547/87507315-91-3-152. PMID: 11266499.
  22. Niroomandi, A. Perrier, M. Bucki, Yohan Payan. Real-time computer modeling in prevention of foot pressure ulcer using patient-specific finite element model and model order reduction techniques. Amit Gefen. Innovations and Emerging Technologies in Wound Care, Elsevier. 2020; 978- 0-12-815028-3: 87-102. 10.1016/B978-0-12-815028-3.00005-5 . hal-02356288
  23. Najafi B, Mohseni H, Grewal GS, Talal TK, Menzies RA, Armstrong DG. An Optical-Fiber-Based Smart Textile (Smart Socks) to Manage Biomechanical Risk Factors Associated With Diabetic Foot Amputation. J Diabetes Sci Technol. 2017;11(4):668-677. doi: 10.1177/1932296817709022. Epub 2017 May 17. PMID: 28513212; PMCID: PMC5588846.
  24. Cruz-Vega I, Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno JdJ, Ramirez-Cortes JM. Deep Learning Classification for Diabetic Foot Thermograms.Sensors. 2020; 20(6):1762. https://doi.org/10.3390/s20061762
  25. Basatneh R, Najafi B, Armstrong DG. Health Sensors, Smart Home Devices, and the Internet of Medical Things: An Opportunity for Dramatic Improvement in Care for the Lower Extremity Complications of Diabetes. J Diabetes Sci Technol. 2018;12(3):577-586. doi: 10.1177/1932296818768618. Epub 2018 Apr 11. PMID: 29635931.
  26. Najafi B, Armstrong DG, Mohler J. Novel wearable technology for assessing spontaneous daily physical activity and risk of falling in older adults with diabetes. J Diabetes Sci Technol. 2013;7(5):1147-60. doi: 10.1177/193229681300700507. PMID: 24124940.
  27. Kulkarni VV. Embedded wearable device for monitoring diabetic foot ulcer parameter. Thesis. 2019 Available from: http://essay.utwente.nl/80212/2/Kulkarni_MA_EEMCS.pdf.
  28. Majumder S, Mondal T, Deen MJ. Wearable Sensors for Remote Health Monitoring.Sensors (Basel). 2017;17(1):130. doi:10.3390/s17010130
  29. Ming A, Walter I, Alhajjar A, Leuckert M, Mertens PR. Study protocol for a randomized controlled trial to test for preventive effects of diabetic foot ulceration by telemedicine that includes sensor-equipped insoles combined with photo documentation. Trials. 2019 Aug 22;20(1):521. doi: 10.1186/s13063-019-3623-x. PMID: 31439007.
  30. Francia P, Anichini R, De Bellis A, Seghieri G, Lazzeri R, Paternostro F, Gulisano M. Diabetic foot prevention: the role of exercise therapy in the treatment of limited joint mobility, muscle weakness and reduced gait speed. Ital J Anat Embryol. 2015;120(1):21-32. PMID: 26738255.
  31. van Schie CH, Noordhof EL, Busch‐Westbroek TE, Beelen A, Nollet F. Assessment of physical activity in people with diabetes and peripheral neuropathy. Diabetes Res Clin Pract 2011;92:e9‐
  32. Lemaster JW, Reiber GE, Smith DG, Heagerty PJ, Wallace C. Daily weight-bearing activity does not increase the risk of diabetic foot ulcers. Med Sci Sports Exerc. 2003 Jul;35(7):1093-9. doi: 10.1249/01.MSS.0000074459.41029.75. PMID: 12840628.
  33. Tulloch J, Zamani R., Akrami M. Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review. (2020). IEEE Access, 8, 198977-199000.
  34. Lazo-Porras M, Bernabe-Ortiz A, Sacksteder KA, et al. Implementation of foot thermometry plus mHealth to prevent diabetic foot ulcers: study protocol for a randomized controlled trial.Trials. 2016;17(1):206. doi:10.1186/s13063-016-1333-1
  35. Maluf KS, Mueller MJ. Novel Award 2002. Comparison of physical activity and cumulative plantar tissue stress among subjects with and without diabetes mellitus and a history of recurrent plantar ulcers. Clin Biomech (Bristol, Avon). 2003;18(7):567-75. doi: 10.1016/s0268-0033(03)00118-9. PMID: 12880704.
  36. Sawacha Z, Gabriella G, Cristoferi G, Guiotto A, Avogaro A, Cobelli C. Diabetic gait and posture abnormalities: a biomechanical investigation through three dimensional gait analysis. Clin Biomech (Bristol, Avon). 2009 Nov;24(9):722-8. doi: 10.1016/j.clinbiomech.2009.07.007. Epub 2009 Aug 21. PMID: 19699564.
  37. Allet L, Armand S, de Bie RA, Golay A, Monnin D, Aminian K, Staal JB, de Bruin ED. The gait and balance of patients with diabetes can be improved: a randomised controlled trial. Diabetologia. 2010;53(3):458-66. doi: 10.1007/s00125-009-1592-4. Epub 2009 Nov 17. PMID: 19921145;
  38. Guiotto A, Sawacha Z, Guarneri G, Cristoferi G, Avogaro A, Cobelli C. The role of foot morphology on foot function in diabetic subjects with or without neuropathy. Gait Posture. 2013;37(4):603-10. doi: 10.1016/j.gaitpost.2012.09.024. Epub 2012 Nov 16. PMID: 23159679.
  39. Bus SA, Armstrong DG, Gooday C, Jarl G, Caravaggi C, Viswanathan V, Lazzarini PA; International Working Group on the Diabetic Foot (IWGDF). Guidelines on offloading foot ulcers in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36(1):e3274. doi: 10.1002/dmrr.3274. PMID: 32176441.
  40. Lee M, van Netten JJ, Sheahan H, Lazzarini PA. Moderate-to-Vigorous-Intensity Physical Activity Observed in People With Diabetes-Related Foot Ulcers Over a One-Week Period. J Diabetes Sci Technol. 2019;13(5):827-835. doi: 10.1177/1932296819848735. Epub 2019 May 29. PMID: 31137944.
  41. Armstrong DG, Abu-Rumman PL, Nixon BP, Boulton AJ. Continuous activity monitoring in persons at high risk for diabetes-related lower-extremity amputation. J Am Podiatr Med Assoc. 2001;91(9):451-5. doi: 10.7547/87507315-91-9-451. PMID: 11679626.
  42. Amitrano F, Coccia A, Ricciardi C, Donisi L, Cesarelli G, Capodaglio EM, D'Addio G. Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment. Sensors (Basel). 2020 Nov 23;20(22):6691. doi: 10.3390/s20226691. PMID: 33238448.
  43. de Castro MP, Meucci M, Soares DP, Fonseca P, Borgonovo-Santos M, Sousa F, Machado L, Vilas-Boas JP. Accuracy and repeatability of the gait analysis by the WalkinSense system. Biomed Res Int. 2014;2014:348659. doi: 10.1155/2014/348659. Epub 2014 Feb 20. PMID: 24701570; PMCID: PMC3950554.
  44. Najafi B, Crews RT, Wrobel JS. Importance of time spent standing for those at risk of diabetic foot ulceration. Diabetes Care. 2010;33(11):2448-50. doi: 10.2337/dc10-1224. Epub 2010 Aug 3. PMID: 20682681.
  45. Kwon OY, Mueller MJ. Walking patterns used to reduce forefoot plantar pressures in people with diabetic neuropathies. Phys Ther. 2001;81(2):828-35. doi: 10.1093/ptj/81.2.828. PMID: 11175680.
  46. Francia P, Anichini R, Gulisano, M, Bocchi L, Seghieri G, Puglisi F, Tedeschi A, De Bellis A. The Possible Role of Trekking Poles to Improve Posture, Balance, and Plantar Pressures Distribution in Diabetic Patients. 2017; 66(S1): A170. Available from: https://diabetes.diabetesjournals.org/content/diabetes/66/Supplement_1/A101.full.pdf
  47. Lim S, Kang SM, Kim KM, Moon JH, Choi SH, Hwang H, et al. Multifactorial intervention in diabetes care using real‐time monitoring and tailored feedback in type 2 diabetes. Acta Diabetol 2016;53:189‐ doi: 10.1007/s00592-015-0754-8. Epub 2015 May 5. PMID: 25936739.
  48. Ding S, Schumacher M. Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review. Sensors (Basel). 2016;16(4):589. doi: 10.3390/s16040589. PMID: 27120602; PMCID: PMC4851102.
  49. Armstrong DG, Lavery LA, Holtz-Neiderer K, Mohler MJ, Wendel CS, Nixon BP, Boulton AJ. Variability in activity may precede diabetic foot ulceration. Diabetes Care. 2004;27(8):1980-4. doi: 10.2337/diacare.27.8.1980. PMID: 15277427.
  50. Lemaster JW, Mueller MJ, Reiber GE, Mehr DR, Madsen RW, Conn VS. Effect of weight-bearing activity on foot ulcer incidence in people with diabetic peripheral neuropathy: feet first randomized controlled trial. Phys Ther. 2008;88(11):1385-98. doi: 10.2522/ptj.20080019. Epub 2008 Sep 18. PMID: 18801859.
  51. Lindberg K, Møller BS, Kirketerp-Møller K, Kristensen MT. An exercise program for people with severe peripheral neuropathy and diabetic foot ulcers - a case series on feasibility and safety. Disabil Rehabil. 2020 Jan;42(2):183-189. doi: 10.1080/09638288.2018.1494212. Epub 2018 Oct 7. PMID: 30293458.
  52. Sujaritha M., Sujatha R., Nithya R.A., Nandhini A.S., Harsha N. (2020) An Automatic Diabetes Risk Assessment System Using IoT Cloud Platform. In: Haldorai A., Ramu A., Mohanram S., Onn C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_32
  53. Ctercteko GC, Dhanendran M, Hutton WC, Le Quesne LP. Vertical forces acting on the feet of diabetic patients with neuropathic ulceration. Br J Surg. 1981 Sep;68(9):608-14. doi: 10.1002/bjs.1800680904. PMID: 7272685.
  54. Stokes IA, Faris IB, Hutton WC. The neuropathic ulcer and loads on the foot in diabetic patients. Acta Orthop Scand 1975;46:839‐ DOI:10.3109/17453677508989271
  55. Bauman JH, Brand PW. Measurement of pressure between foot and shoe. Lancet. 1963;1(7282):629-32. doi: 10.1016/s0140-6736(63)91271-6. PMID: 13966981.
  56. Coates J, Chipperfield A, Clough G. Wearable Multimodal Skin Sensing for the Diabetic Foot.Electronics. 2016; 5(3):45. https://doi.org/10.3390/electronics5030045
  57. Cristiani, A., Bertolotti, G.M., Marenzi, E., & Ramat, S. An Instrumented Insole for Long Term Monitoring Movement, Comfort, and Ergonomics.IEEE Sensors Journal. 2014;14, 1564-1572. DOI:1109/JSEN.2014.2299063
  58. Botros, Fady S., Mona F. Taher, Naglaa M Elsayed and A. Fahmy. “Prediction of diabetic foot ulceration using spatial and temporal dynamic plantar pressure.”2016 8th Cairo International Biomedical Engineering Conference (CIBEC) (2016): 43-47. DOI:1109/CIBEC.2016.7836116
  59. Ferber R, Webber T, Everett B, Groenland M. Validation of plantar pressure measurements for a novel in-shoe plantar sensory replacement unit. J Diabetes Sci Technol. 2013;7(5):1167-75. doi: 10.1177/193229681300700535. PMID: 24124942; PMCID: PMC3876359.
  60. Abbott CA, Chatwin KE, Foden P, Hasan AN, Sange C, Rajbhandari SM, Reddy PN, Vileikyte L, Bowling FL, Boulton AJM, Reeves ND. Innovative intelligent insole system reduces diabetic foot ulcer recurrence at plantar sites: a prospective, randomised, proof-of-concept study. Lancet Digit Health. 2019;1(6):e308-e318. doi: 10.1016/S2589-7500(19)30128-1. Epub 2019 Sep 26. PMID: 33323253.
  61. Bassett DR Jr, Toth LP, LaMunion SR, Crouter SE. Step Counting: A Review of Measurement Considerations and Health-Related Applications.Sports Med. 2017;47(7):1303-1315. doi:10.1007/s40279-016-0663-1. PMID: 28005190.
  62. Waaijman R, Keukenkamp R, de Haart M, Polomski WP, Nollet F, Bus SA, et al. Adherence to wearing prescription custom‐made footwear in patients with diabetes at high risk for plantar foot ulceration. Diabetes Care 2013;36:1613‐ doi: 10.2337/dc12-1330. Epub 2013 Jan 15. PMID: 23321218.
  63. Kluding PM, Singleton JR, Pasnoor M, Dimachkie MM, Barohn RJ, Smith AG, et al. Activity for diabetic polyneuropathy (ADAPT): Study design and protocol for a 2‐site randomized controlled trial. Phys Ther 2017;97:20‐ doi: 10.2522/ptj.20160200. PMID: 27417167.
  64. Jao YL, Gardner SE, Carr LJ. Measuring Weight-Bearing Activities in Patients With Previous Diabetic Foot Ulcers. J Wound Ostomy Continence Nurs. 201;44(1):34-40. doi: 10.1097/WON.0000000000000270. PMID: 27556347.
  65. Dasanayake IS, Bevier WC, Castorino K, Pinsker JE, Seborg DE, Doyle FJ 3rd, Dassau E. Early Detection of Physical Activity for People With Type 1 Diabetes Mellitus. J Diabetes Sci Technol. 2015;9(6):1236-45. doi: 10.1177/1932296815592409. PMID: 26134831
  66. Yoong NKM, Perring J, Mobbs RJ. Commercial Postural Devices: A Review. Sensors (Basel). 2019;19(23):5128. doi: 10.3390/s19235128. PMID: 31771130; PMCID: PMC6929158.
  67. Yap MH, Chatwin KE, Ng CCet al. A new mobile application for standardizing diabetic foot images. J Diabetes Sci Technol. 2018; 12: 169-173
  68. Rghioui A, Lloret J, Sendra S, Oumnad A. A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms.Healthcare (Basel). 2020;8(3):348. Published 2020 Sep 19. doi:10.3390/healthcare8030348
  69. Ara A, Ara A. Case study: Integrating IoT, streaming analytics and machine learning to improve intelligent diabetes management system.2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017; 3179-3182, doi: 10.1109/ICECDS.2017.8390043.
  70. Ahad A, Tahir M, Aman Sheikh M, Ahmed KI, Mughees A, Numani A. Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review. Sensors (Basel). 2020;20(14):4047. doi: 10.3390/s20144047. PMID: 32708139; PMCID: PMC7411917.
  71. Mattmann CA. Computing: A vision for data science. Nature. 2013;493(7433):473-5. doi: 10.1038/493473a. PMID: 23344342.
  72. Dolezel D, McLeod A. Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation.Perspect Health Inf Manag. 2019;16(Summer):1a. PMID: 31423120.
  73. Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Comput Struct Biotechnol J. 2017;15:104-116. doi: 10.1016/j.csbj.2016.12.005. PMID: 28138367; PMCID: PMC5257026.
  74. Sudarvizhi, M.D., et al., Identification And Analysis Of Foot Ulceration Using Load Cell Technique. IRJET; 2019; 7792-7797. Available from: https://www.irjet.net/archives/V6/i3/IRJET-V6I3937.pdf
  75. See CK, Acharya UR, Zhu K, Lim TC, Yu WW, Subramaniam T, Law C. Automated identification of diabetes type-2 subjects with and without neuropathy using eigenvalues. Proc Inst Mech Eng H. 2010;224(1):43-52. doi: 10.1243/09544119JEIM614. PMID: 20225456.
  76. Acharya U, R., Tan, P.H., Subramaniam, T. et al. Automated Identification of Diabetic Type 2 Subjects with and without Neuropathy Using Wavelet Transform on Pedobarograph. J Med Syst 32, 21–29 (2008). https://doi.org/10.1007/s10916-007-9103-y.
  77. Hassan ZM. Mobile phone text messaging to improve knowledge and practice of diabetic foot care in a developing country: Feasibility and outcomes. Int J Nurs Pract. 2017;23(1). doi: 10.1111/ijn.12546. PMID: 28635062.
  78. Nesari M, Zakerimoghadam M, Rajab A, Bassampour S, Faghihzadeh S. Effect of telephone follow-up on adherence to a diabetes therapeutic regimen. Jpn J Nurs Sci. 2010;7(2):121-8. doi: 10.1111/j.1742-7924.2010.00146.x. PMID: 21092015.
  79. Shin L, Bowling FL, Armstrong DG, Boulton AJM. Saving the Diabetic Foot During the COVID-19 Pandemic: A Tale of Two Cities. Diabetes Care. 2020;43(8):1704-1709. doi: 10.2337/dc20-1176. Epub 2020 Jun 12. PMID: 32532755.
  80. Pataky Z, Grivon D, Civet Y, Perriard Y. Chaussures intelligentes pour patients diabétiques [Intelligent footwear for diabetic patients]. Rev Med Suisse. 2016;12(502):143-7. PMID: 26946791. Available from:https://www.revmed.ch/RMS/2016/RMS-N-502/Diabete.-Chaussures-intelligentes-pour-patients-diabetiques
  81. Duruturk N. Telerehabilitation intervention for type 2 diabetes. World J Diabetes. 2020 Jun 15;11(6):218-226. doi: 10.4239/wjd.v11.i6.218. PMID: 32547696.
  82. De Groot J, Wu D, Flynn D, Robertson D, Grant G, Sun J. Efficacy of telemedicine on glycaemic control in patients with type 2 diabetes: A meta-analysis. World J Diabetes2021; 12(2): 170-197. doi: 4239/wjd.v12.i2.170 .
  83. Monteiro RL, Sartor CD, Ferreira JSSP, Dantas MGB, Bus SA, Sacco ICN. Protocol for evaluating the effects of a foot-ankle therapeutic exercise program on daily activity, foot-ankle functionality, and biomechanics in people with diabetic polyneuropathy: a randomized controlled trial. BMC Musculoskelet Disord. 2018 Nov 14;19(1):400. doi: 10.1186/s12891-018-2323-0. PMID: 30428863;
  84. Kluding PM, Bareiss SK, Hastings M, Marcus RL, Sinacore DR, Mueller MJ. Physical Training and Activity in People With Diabetic Peripheral Neuropathy: Paradigm Shift. Phys Ther. 2017 Jan 1;97(1):31-43. doi: 10.2522/ptj.20160124. PMID: 27445060;
  85. Dijs HM, Roofthooft JM, Driessens MF, De Bock PG, Jacobs C, Van Acker KL. Effect of physical therapy on limited joint mobility in the diabetic foot. A pilot study. J Am Podiatr Med Assoc. 2000;90(3):126-32. doi: 10.7547/87507315-90-3-126. PMID: 10740995.
  86. Grewal GS, Sayeed R, Schwenk M, Bharara M, Menzies R, Talal TK, Armstrong DG, Najafi B. Balance rehabilitation: promoting the role of virtual reality in patients with diabetic peripheral neuropathy. J Am Podiatr Med Assoc. 2013;103(6):498-507. doi: 10.7547/1030498. PMID: 24297986.
  87. Lee S, Shin S. Effectiveness of virtual reality using video gaming technology in elderly adults with diabetes mellitus. Diabetes Technol Ther. 2013;15(6):489-96. doi: 10.1089/dia.2013.0050. Epub 2013 Apr 5. PMID: 23560480.
  88. Francia P, Anichini R, Seghieri G, De Bellis A, Gulisano M. History, Prevalence and Assessment of Limited Joint Mobility, from Stiff Hand Syndrome to Diabetic Foot Ulcer Prevention: A Narrative Review of the Literature. Curr Diabetes Rev. 2018;14(5):411-426. doi: 10.2174/1573399813666170816142731. PMID: 28814244.
  89. Francia P, Toni S, Iannone G, Seghieri G, Piccini B, Vittori A, Santosuosso U, Casalini E, Gulisano M. Type 1 diabetes, sport practiced, and ankle joint mobility in young patients: What is the relationship? Pediatr Diabetes. 2018;19(4):801-808. doi: 10.1111/pedi.12643. Epub 2018 Mar 1. PMID: 29493073.
  90. Francia P, Anichini R, Seghieri G, De Bellis A, Gulisano M. History, Prevalence and Assessment of Limited Joint Mobility, from Stiff Hand Syndrome to Diabetic Foot Ulcer Prevention: A Narrative Review of the Literature. Curr Diabetes Rev. 2018;14(5):411-426. doi: 10.2174/1573399813666170816142731. PMID: 28814244.
  91. Francia P, Toni S, Iannone G, Seghieri G, Piccini B, Vittori A, Santosuosso U, Casalini E, Gulisano M. Type 1 diabetes, sport practiced, and ankle joint mobility in young patients: What is the relationship? Pediatr Diabetes. 2018;19(4):801-808. doi: 10.1111/pedi.12643. Epub 2018 Mar 1. PMID: 29493073.
  92. Ossebaard, H., Adrie de Bruijn, J. Gemert-Pijnen and R. Geertsma. “Risks related to the use of eHealth technologies : An exploratory study.” 2013. Available from: https://www.rivm.nl/bibliotheek/rapporten/360127001.pdf
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