Mobile health support for motor disability individuals: A review focused on holistic assessment and interventions

Abstract

BACKGROUND:

Health is a complex concept that involves diverse dimensions, such as physical condition, psychological state, and social relationships. Several mobile applications are being used to assess these dimensions and provide recommendations for behaviour changes. However, few studies consider the particularities of the motor disability (MD) population.

OBJECTIVE:

To identify (1) the current mobile support for MD individuals; (2) the health dimensions that are assessed; (3) if they consider specific interventions for the motor disability population; and (4) if health dimensions are holistically rather than individually considered to provide support for MD mobile users.

METHOD:

A systematic review was conducted to identify studies from the literature that could answer a pre-defined set of research questions.

RESULTS:

Fifteen from 111 initial studies were included in this review and they show that the mobile support presents several limitations, such as the lack of a multidimensional holist assessment and intervention process, when they intend to promote aspects of rehabilitation or prevention for the MD population.

CONCLUSIONS:

This study shows that mobile technology has a high potential to assist its users in managing overall dimensions that affect their health conditions. However, aspects of customization, holist reasoning and automated interventions still need to be considered.

Keywords

Motor disability mHealth multidimensional assessment mobile intervention

1. Introduction

Motor disability (MD) is a condition that involves different levels of limitations, which mainly decrease the ability to activate the muscle and produce movement. Thus, the functional capacity of individuals to carry out their daily living tasks is negatively affected and these limitations also increase the risk of other avoidable chronic conditions such as cardiovascular disease, diabetes, cancer, osteoporosis, and depression, with significant negative effects on the overall quality of life [1]. The work of van den Berg and colleagues [2] presents an epidemiological review that shows the worldwide quantitative perspective of the spinal cord injury condition, which is one of the main causes of MD, and stresses the social and economic impact of this type of injury. For example, they show that the annual crude incidence rates varied from 12.1 per million in The Netherlands to 57.8 per million in Portugal. Furthermore, there is a trend towards increased incidence in the elderly population, likely due to falls and non-traumatic injury. Previous studies [3] also show that the main current aim of MD rehabilitation has shifted from the extension of life expectancy to the enhancement of independence and quality of life [1]. Therefore, technology is an important ally in this task and several studies have proposed strategies to facilitate the daily activities of MD individuals [4] and assist their process of rehabilitation [5].

The mobile technology for health, which is commonly associated with the term mHealth, brings several resources that can be used, for example, to assess the multidimensional parameters (e.g. nutritional behaviour, level of physical activity, stress, sleeping quality, etc.) that affect the health conditions of individuals [6]. Moreover, it can provide just in time (right support at the right moment/context and in the right amount) and customised (based on an individual’s performance and goal) assessment and interventions. While the literature already discusses several applications of mHealth concerning nutrition [7], physical activities [8], and stress [9] aspects; the particular use of mHealth for MD individuals is not clear. For example, the assessment regarding the level of physical activities and related recommendations for MD individuals are completely different when compared with the non-MD public.

The present study aims to characterize the current state of the art on the use of mobile technology as support to the health maintenance of MD individuals. Therefore, it identifies the main limitations of the area and opportunities for future studies, mainly stressing the importance of a multidimensional health assessment and holist intervention.

2. Material and methods

A systematic review was used as the method to answer the following question: “Which mobile support is being used to improve the health conditions of the MD public?”. This more general question was divided into four simpler research questions, which guided the process of paper selection. These questions were:

RQ ${}_{1}$ : Which are the current issues considered by mobile technology to support MD individual? RQ ${}_{2}$ : Which are the health dimensions that are assessed? RQ ${}_{3}$ : Are there interventions that consider the MD special needs? RQ ${}_{4}$ : Are the dimensions holistically analysed to provide interventions?

After the definition of the research questions, a search string was specified using terms that are related to such questions. This string was refined through initial attempts and its final specification was: (“spinal cord injury” OR paralysis OR tetraplegia OR quadriplegia OR paraplegia OR “motor disability” OR “motor impairment”) AND (health OR intervention OR assessment OR rehabilitation OR medical) AND (mobile OR smartphone). The string was then used in three databases: IEEE Xplore Digital Library, ACM Digital Library, and PubMed. While the two former datasets are the main search engines in the area of technology; the latter seems to be the main dataset in the health literature. Afterward, three stages for paper selection were conducted:

Selection of studies based on search string (Stage 1): studies that contain the pre-defined search string in the title or abstract, written in English, date from 2009 to 2019, and they are not editorials, prefaces, discussions, comments, summaries of tutorials, workshops, abstracts, and panels; Screening of title and abstract (Stage 2): quick checking of studies to verify if they, in fact, cover aspects of our research area. Only primary studies are considered so that informal or systematic reviews are dropped out, together with duplicated papers; Relevance analysis (Stage 3): the full text is analysed along the data extraction to verify if the paper brings enough information to answer one or more research questions. Otherwise, the paper is also dropped out of the study.

The temporal search range was defined from 2009 to 2019 since our pilot attempts did not return relevant studies before 2009. Two reviewers independently assessed each article to decide by its inclusion/exclusion (Stages 2 and 3) and disagreements between reviewers were resolved by discussion and consensus.

Apart from the scientific papers, we could also extend this review considering mobile applications since a few of them provide support for the MD population. For example, the “Wheelchair Exercises” is an Android fitness app that tells about different aerobic exercises that can be done on wheelchairs; while the “Weight Reduction in Wheelchair” helps all individuals especially those confined to wheelchairs to reduce weight. Both applications can be found in the Google Play Store. However, these applications do not present proper documentation, which is the main source of information for systematic reviews. For example, their fundaments and forms of validation are not available. Thus, their analysis would require the installation and practical experimentations to the understanding of their main features. This fact is against one of the principles of systematic reviews, which states that the inclusion of an item depends on whether particular outcomes of interest have been reported and in an appropriate, consistent manner. The outcomes can be excluded if they are self-reported rather than using objective measures.

3. Results

Table 1 shows the number of studies that were selected after the application of each selection process: search string (Stage 1), screening of title and abstract (Stage 2), and relevance analysis (Stage 3). According to this table, 15 studies were selected to answer the 4 research questions. The summary of the answers is presented in Table 2.

Table 1
Papers selection process

Database	Number of paper selected			References
	Stage 1	Stage 2	Stage 3	(Stage 3)
IEEE Xplore	7	4	3	[10, 11, 12]
ACM Library	42	14	5	[13, 14, 15, 16, 17]
PubMed	62	12	7	[18, 19, 20, 21, 22, 23, 24]

Table 2

Research questions and summary of answers

Ref./Year	RQ ${}_{1}$ – function	RQ ${}_{2}$ – data source	RQ ${}_{3}$ – recommendations	RQ ${}_{4}$ – holistic analysis
[10] 2016	Posture monitoring	Accelerometer	No	No, unique dimension
[11] 2014	Severity identification	Touchscreen, gyroscope	No	No, unique dimension
[12] 2015	Freezing detection	Accelerometer, gyroscope	No	No, unique dimension
[13] 2016	Posture monitoring	Accelerometer, gyroscope, magnetic sensor	Visual guidance to fix the posture	No, unique dimension
[14] 2014	Motor skills improvement	Simple game tap interaction	No	No, unique dimension
[15] 2016	Posture monitoring	Smart textiles	No	No, unique dimension
[16] 2019	Motor skills detection	Gestural interactions	No	No, unique dimension
[17] 2014	Rehabilitation	Video images, heart rate via chest strap	Asynchronous and synchronous supervision, with instructions delivered by videos	Activity of users together with their heart rate
[18] 2019	Self-management prevention	Events agenda, questionnaires	Treatment plan	Integrated architecture but dimensions seem to be individually analysed
[19] 2019	Self-management prevention	Events agenda, questionnaires	Treatment plan	Integrated architecture but dimensions seem to be individually analysed
[20] 2016	Rehabilitation	Inertial sensors on the ankles and heart rate for activities characterization	Therapist contacts subjects once a week to offer summary feedback	Several variables related to physical aspects
[21] 2019	Pressure injury prevention	Pressure mat	No	Unique dimension
[22] 2019	Rehabilitation	Answers manually included for health issues. Creation of health storylines	States goals to be reached by users	No, several dimensions but solutions separately given for each dimension
[23] 2016	Energy expenditure control	Gyroscope	No	No, focus only on the physical dimension
[24] 2018	Rehabilitation	Answers manually included for health issues. Creation of health storylines	States goals to be reached by users	No, several dimensions but solutions separately given for each dimension

The first research question (RQ ${}_{1}$ ) was: Which are the current issues considered by mobile technology to support MD individuals? The approaches were divided into two groups, according to the issues that they consider. The first group considers problem-focused approaches, which intend to propose solutions for a specific issue such as posture monitoring [10, 13, 15], identification of MD severity [11, 16], freezing of gait detection [12], improvement of motor precision [14], pressure injury prevention [21], and energy expenditure monitoring [23]. Differently, the second group considers the process of rehabilitation [17, 20, 22, 24] or prevention [18, 19] in a more general sense. The study in [17], for example, proposes a six-steps methodology for neuro-rehabilitation that is supported by a cloud-based platform. The whole rehabilitation is home-based and guided by a mobile application. The proposal in [20] suggests the use of mobile health to promote behavioural training that includes education about the effects of practices and exercise. Thus, users could become more independent and develop self-management strategies for their rehabilitation. The studies in [22, 24] evaluate the efficacy of promoting rehabilitation by means of a third-party iOS mobile application. Differently, the studies in [18, 19] focus on the development of their own solution, which is based on an architecture that can integrate several application modules aimed to decrease the risk for secondary medical complications, which are common in the MD population.

The second research question (RQ ${}_{2}$ ) is: Which are the health dimensions that are assessed? To answer this question, we must verify the sensors and inputs that are used in each approach. Therefore, the work in [17] uses a combination of video (MD individuals use the mobile camera to record their physical activities) and heart rate, which are sent via mobile devices to experts. The works [17, 18] are based on the same mHealth system (iMHere). Its first version was used to assess five dimensions: medication intake, bowel management, bladder, mood, and skin problems. The second iMHere version was extended with other types of assessments and the main examples are physical activities (library of 49 exercises – users must select exercises and include its duration), nutrition (library of foods/drinks – users must select foods and include their amount), and personal health record (e.g. allergies, surgical history, etc.). Similarly, the works [22, 24] discuss the importance of mobile technology as a support for rehabilitation, using the SCI Health Storylines ${}^{\text{TM}}$ app as a case study. This app conducts the assessment of bowel, bladder, mood, and abnormal health episodes (e.g. high blood pressure, uncommon heart rate, headaches, etc.). The idea is to construct a health storyline of users that is then sent to the analysis of experts. All these assessments are conducted by means of questionnaire-like approaches. The work in [20] focuses on the physical dimension aspect, considering assessments such as heart rate with exercise, periods of sedentary time, usual walking speed and distance per bout at home and outdoors, gait cycle symmetry, and length of bouts of activity. Apart from these more complex studies, the problem-focused approaches use a combination of accelerometers [10, 12, 13], gyroscopes [11, 12, 13, 23], touchscreen (to obtain patterns of interaction) [11, 14, 16], smart textiles [15], magnet sensors [13], and pressure mat [21] to obtain information about the physical parameters (e.g. position and movements) of their users.

The third research question (RQ ${}_{3}$ ) was: Are there interventions that consider the MD special needs? Several of the applications only consider the question of awareness. This means, they warm on the status of some dimension, such as posture [10] or impairment severity [11]. Differently, the studies related to rehabilitation and prevention [17, 18, 19, 20, 22, 24] send the collected data to experts, which return specific interventions via mobile devices. The difference among these approaches is mainly the intervention content. For example, this content is represented by videos that show how to do or correct a physical activity [17], messaging communication that includes treatment plans [18, 19], weekly summary feedback [20], and different goals to several dimension [22, 24]. Another important feature of these interventions is their asynchronous feature. While some proposals also enable the schedule of virtual meetings [17]; the interventions of these proposals are mostly delivered after the offline analysis of the acquired data. For example, the studies in [22, 24] employ a mobile application to acquire data that is used to construct a dynamic health storyline of users. The generation of interventions, in the form of goals, are then based on these storylines and periodically delivered to mobile uses. Thus, a strong sense of customization, concerning the interventions, is used to attend the particularities of each user.

A very important aspect related to the interventions is the theme of the next question (RQ ${}_{4}$ ): Are the dimensions holistically analysed to provide interventions? This is not the case of the problem-focused approaches since they deal with simple dimensions. However, this holistic multidimensional analysis is also not observed in the remaining studies. Even when these studies consider several dimensions (e.g. physical activities, mood, nutrition) [22, 24], interventions are separately given to each of such dimensions. In order, as the interventions are specified by human experts, this kind of holist analysis is very dependent on the knowledge of such experts. The studies in [18, 19] present a first step towards the holist analysis when they propose an architecture that acts as a backbone to the plugin of application modules. As the applications share the same interfaces, the data generated by these applications could be integrated and support the execution of holistic processes. This scenario is very similar to mobile apps that are similar to the iOS application used in [22, 24]. This application is, in fact, a platform to aggregate several simpler modules that collect health information from their users. However, a holistic analysis of this information is not also provided.

4. Discussion

The mobile technology is mainly able to promote customized and just in time interactions. This review shows that customization is already considered in several studies, such as in [18, 19], which focus on supporting personalized and evolving self-care needs. However, the just in time feature is not well explored in the studies on rehabilitation. In fact, interventions are provided asynchronously because they depend on the availability of human experts. While the assistance of experts brings a strong sense of customization concerning the interventions; such an approach is not scalable and the low availability of experts may become a bottleneck. Therefore, approaches to promote long-term self-management of skills practice, such as strengthening and fitness [20], are important to create a sense of independence.

A further feature of mobile devices is their ability to transparently acquire information. This is supported by diverse sensors that are currently available in the devices. The review shows that the problem-focused approaches take advantage of these sensors, but the majority of the applications consider traditional questionnaires as the main source of input data. This may not be adequate for mobile users, mainly considering the MD population. For example, the level of stress could be transparently obtained using the camera or pattern of interactions rather than using a Psychology-based questionnaire. The work in [7] also discusses another alternative approach concerning the use of traditional questionnaires, in this case, aimed at nutrition assessment. This solution is based on an interactive mobile version of the Food Frequency Questionnaire (FFQ) and it provides an easy way to indicate frequencies and portion sizes for foods that users have consumed during a period of time. Moreover, while some works use further hardware to assess health parameters of mobile users (e.g. smart textiles [15] and pressure mat [21]), the work in [33] shows that the cost of acquiring other devices may be prohibitive to the lower social classes, where the higher number of people with motor impairments are found. A different issue is the employment of passive approaches for some dimensions, such as nutrition. The study in [22] uses a proprietary application that has a nutritional planner as part of their resources. However, it is just a way to link users and experts. They do not present any automatic process to characterize the food in their macro (e.g. fat, carbohydrates) and micro (e.g. vitamins and minerals) elements. This idea of automation can be extended to all health dimensions in mobile applications that intend to support the MD population. The present review shows that automatic warms are only used in simple situations, such as to correct the posture [13]. Currently, the literature already brings specific guidelines for the MD population, such as for physical activities [25], whose content could be used to automate the generation of interventions. This approach could create a more synchronous interaction between users and mobile devices. Therefore, the automatic process of generating interventions, based on the assessment of the health status of individuals, seems to be the next stage in this discussion. One of the main advantages of this automation is the computational representation of the knowledge about the needs of the MD population and its scalable use to mitigate the workload on experts.

The review also shows that current approaches do not consider the multidimensional feature of health problems in a holistic way. This is an important lack since the health dimensions have a high correlation and cannot be analysed in isolation. For example, an app for physical activity intervention can indicate a health programme for losing weight. However, studies [26, 27] demonstrated that even when exercise energy expenditure is high, a healthy diet is still required for weight loss to occur in many people. Thus, the assessment of physical activity and nutritional dimensions should be conducted together. However, this may not still be enough. Even if individuals have good behaviour in both dimensions, a stressful life, associated with negative psychological conditions, liberates an extra amount of cortisol that signals the body to shift metabolism to store fat. Thus, all these dimensions should be evaluated together so that interventions are not individually delivered to each specific issue.

Some applications of the Android and iOS platforms already consider the multidimensional health assessment of individuals. These applications are not exclusive for the MD population, but they could be used as a general way to assess gross data from their users. The SCI Health Storylines ${}^{\text{TM}}$ app is one of these applications and its efficacy was evaluated considering the needs of the MD population [22, 24]. Unfortunately, the literature does not provide similar evaluations with other mobile applications.

5. Research opportunities

The main limitations indicated in the previous section (just in time interventions, automatic feedback, and multidimensional analysis) may be a consequence of the very recent interest in using mobile technology as a resource to support the MD population. According to this review, 4 from 6 papers [17, 18, 19, 20, 22, 24] that discuss more complex mobile support for MD rehabilitation were published in 2018/2019. Thus, mHealth for MD rehabilitation still needs to consider the trend of holist and automated computational-assisted interpretation of data. An important example in this direction is the effort of the H2020 NESTORE project [28], which was recently funded by the EU Commission and has defined a set of ontologies that includes three core health dimensions: Physical/Physiological, Nutritional, Cognitive/Mental/Social. While the focus of the NESTORE project is on the ageing population, its models are generic so that it can support a second layer of knowledge-based reasoning aimed at different groups, such as the MD population.

One of the main advantages of using the ontology framework, concerning the multidimensional analysis of health parameters, is its logical formalism that supports several types of reasoning processes. However, the creation of an integrated ontology for MD support is not an easy task due to the number of concepts involved. The guide to creating ontologies, proposed by Noy and McGuinness [29], suggests that the reuse of domain knowledge is one of the driving forces behind the ontology research. According to the authors, if one group of researchers develops such an ontology in detail, others can simply reuse it for their domains. Additionally, if we need to build a large ontology, several existing ontologies describing portions of the large multidimensional domain could be integrated. We can also reuse a general ontology and extend it to describe our domain of interest. The literature on ontology integration presents several approaches that can be useful in such a task. An example of research in this direction is the work of Caldarola e Rinaldi [30], which presents an approach to ontology reuse based on heterogeneous matching techniques and how the process of ontology construction is improved and simplified by automatizing the selection and the reuse of existing data models. A further challenge in the ontological representation is to verify if the traditional ontology semantics are enough to describe the mHealth domain for MD support. While ontologies have been successfully used as part of expert systems, they present some limitations since they are based on the classical two-valued or Boolean semantics, which cannot directly manage imprecise or vague pieces of knowledge that are inherent to several real-world problems [31]. This may be the case of the mHealth support domain, since several of its definitions, such as stress or physical health, are imprecise or subjective. To overcome this limitation, several extensions based on fuzzy logic to classical ontologies have been proposed [32] and the use of this strategy may be the case in the context of the present domain. Therefore, there is still an ample set of opportunities to advance the mHealth support for MD individuals and transform their mobile devices in personalised virtual assistants that provide holist rather than isolated assessments and interventions.

6. Conclusion

The executive summary of the World Health Organization (WHO) stresses that “The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe”. To reach this potential, research in mHealth must observe the specific needs of each population group. This present review showed that the support for the MD population is still in an initial stage, indicating some limitations that should be addressed in future researches. These researches must necessarily consider issues concerning holist assessment and interventions, which could be coped with approaches of knowledge-based systems such as the use of ontologies [28] and reasoning strategies. This is the direction that our group intends to use as the sequence of this study.

Footnotes

Acknowledgments

The authors would like to thank professor Dr Faustina Hwang from the Department of Biomedical Engineering, University of Reading (UK), for the important discussions related to this study; and the National Council for the Improvement of Higher Education (CAPES) for the financial support by means of the Capes-PrInt programme.

Conflict of interest

None to report.

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Mobile health support for motor disability individuals: A review focused on holistic assessment and interventions

Abstract

BACKGROUND:

OBJECTIVE:

METHOD:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Material and methods

3. Results

Table 1 Papers selection process

5. Research opportunities

6. Conclusion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Papers selection process