MOBIlity assessment with modern TEChnology in older patients’ real-life by the General Practitioner: the MOBITEC-GP study protocol

Demographic transformation and increasing life expectancy in industrialized countries comes along with a growing number of older adults suffering from chronic health conditions, such as osteoarthritis, coronary heart disease, diabetes mellitus, or dementia [1]. The proportion of patients with “multimorbidity”, usually defined as the co-occurrence of at least two chronic conditions [2], grows accordingly. Figures on the prevalence of multimorbidity among over 65-year-olds vary between 40 and 85% [2‐5]. Multimorbidity is strongly associated with poor clinical outcomes, including reduced quality of life [6], increased risk of inappropriate medication with major side-effects [5, 7] as well as high mortality and disability rates [8, 9]. The burden of disease is substantial for the affected patients and their relatives, and also for the healthcare system [10‐12].

The simple definition of multimorbidity hardly accounts for the complex relationships between concurrent chronic diseases and the difficulties in managing multimorbid patients. It has been recognized that these patients’ needs are insufficiently served by traditional ways of health care provision [12‐14]. One reason is the disease-oriented rather than integrated approach towards managing patients with multimorbidity [15, 16]. Disease-specific guidelines are often contradictory and impractical when applied to multimorbid patients. Disease-oriented management often results in polypharmacy and knowledge of potential side effects, hazards and harm of interventions is insufficient [15]. This lack of knowledge is even more striking, as it is not clear whether traditional improvements in outcomes, for example mortality, are attainable and desirable in patients with multimorbidity [16]. Patient and relative preferences are frequently unmet, as are patient-oriented outcomes such as improving physical functioning and maintaining independence [17, 18]. There is even evidence that the association between multimorbidity and mortality is lost when adjusted for functional impairment [19]. Hence, there is increasing awareness of the importance of physical functioning as a basic integrator of older adults’ health and as a major health outcome; optimizing functional status has been recognized as a central goal for all persons with chronic illness [20].

“Mobility” is a central element of physical functioning [21, 22]. It has been defined comprehensively as “the ability to move oneself (either independently or by using assistive devices or transportation) within environments that expand from one’s home to the neighbourhood and to regions beyond” ([23], p. 444). Hence, measures that have been used to characterize a person’s mobility include tests of “physical function” [24], and assessments of “life space” [25].

Lower extremity physical function can be measured by simple tests such as stop-watch measured gait speed, balance measures such as maintaining the tandem stand for 10 s, or simple assessments of muscle power such as timing of 5 sit-to-stand cycles [24, 26]. In relation to a person’s mobility, gait speed is one of the most central functional parameters. In prospective studies, poor physical function as well as reduced gait speed alone have been shown to be highly predictive of falls [27, 28], dependency in basic activities of daily living [29‐31], healthcare utilization [32‐34], and mortality [35, 36]. Reduced physical function has been shown to foster social isolation [37], anxiety and depression [38], and to be associated with an overall reduced quality of life [39]. Based on the existing evidence from large-scale prospective studies, clinical cut-offs for functional measures, indicating an increased risk for falls or an increased risk for mobility disability, have been established [40‐42], so that these measures have found their way into routine geriatric inpatient care and rehabilitation settings.

Life-space, the “spatial extent in which a person moves within a specified period” ([43], p. 155), encompasses “the interaction between intrinsic capabilities of the person and the demands of the extrinsic environment” ([43], p. 155). It has been shown that restricted life-space is predictive of disability in activities of daily life (ADL) [44], nursing home admission [45] and mortality [46]. Until now, epidemiological studies mostly relied on questionnaires to measure life-space. However, the geospatial information gained from questionnaires is rather crude. As an example, the University of Alabama at Birmingham (UAB) Study of Aging Life-Space Assessment (LSA) assesses the extent of a person’s movement (within the past 4 weeks) categorized into 5 spatial levels, ranging from the participant’s bedroom to places outside the participants’ home town [25]. While the independent predictive value of life-space on different health-related outcomes has been shown repeatedly [45‐48], recommendations on clinical cut-offs are currently sparse (e.g .[ 49]) and measures of life-space have not yet found their way into clinical practice.

Measures of functional status have been suggested to inform health care payment systems [50] and they are central components of decision trees advocated by current fall prevention guidelines [27, 41]. In early stages of decline, physical function can be stabilized or even reversed by targeted intervention such as exercise programmes [51]. Life-space, and thereby participation in social life, can be maintained by adapting the environment to the patients’ needs (e.g. by providing assistive devices, adapting patients’ homes or providing social support). Similar to chronic illness, physical functioning is dynamic in nature [52]; mobility should therefore be routinely monitored to account for the dynamic interactions between physiological systems and the environments of daily life over time. It has even been suggested that functional status should be considered the “sixth vital sign” in addition to the conventional vital signs (body temperature, pulse etc.) [20, 53, 54].

As mentioned, physical function tests have found their way into routine geriatric inpatient care and rehabilitation settings, but general practitioners (GPs) do not routinely assess physical function as part of their older patients’ management [55, 56]. This deficiency in administering assessments might occur for a number of reasons: physical function assessments (e.g. the measurement of 4 m gait speed) require standardized equipment, space and training; they are time- and therefore cost-intensive. There is a lack of reimbursement and particularly in patients with multiple chronic conditions, GPs have to weigh the realization of functional assessments against other preventive and therapeutic services [57, 58]. These considerations are in contrast to the ideal position that GPs would have to monitor their older patients’ mobility. GPs are among the few persons who have regular access to community-dwelling and mobility-limited older adults [59, 60]; they often have established long-lasting, ongoing and trusting relationships with their older patients [61]. GPs are also able to appraise the results of a mobility assessment against the background of their patients’ overall health status and medical history.

Modern technologies such as Global Navigation Satellite Systems (GNSS) – including various satellite systems such as GPS, GLONASS, Galileo and Beidou – and inertial measurement units (IMUs) – including accelerometer, gyroscope and magnetometer – offer the chance to implement measures of mobility (physical function and life-space) into routine primary health care and to follow-up on older patients’ mobility over time. In contrast to traditional assessment tools, these technologies allow for observation of older adults function in real-life settings and in interaction with their environment [62, 63]. It has been criticized in the past that laboratory-based functional assessments only have limited value in predicting older adults’ real-life mobility behaviour [64]. GNSS technology and IMUs are nowadays embedded in every modern smartphone, a device that has become popular and shows much promise in improving the health care of older adults. High availability, objectivity and low additional costs might facilitate widespread future use.

While high-end, handheld GPS devices can achieve decimetre accuracy, typical consumer GPS devices or smartphones will achieve best-case accuracies of 2–3 m under normal conditions [65]. Latest smartphone models are also meant to achieve decimeter-level positioning accuracy in post-processing mode, due to the ability to use dual frequency GPS/GALILEO [66]. That is, consumer-level products are starting to reach a positioning accuracy level where accurate and robust movement parameter derivation (e.g. distance, speed, acceleration etc.) is becoming feasible, particularly for distances that exceed the GPS error by at least one order of magnitude, that is, several dozens to some hundred meters. So far, however, the use of GPS-derived movement parameters to quantify physical performance has mainly been limited to team sports [67, 68]. In the health sciences, most applications have been confined to using GPS fixes to estimate individuals’ life-spaces [69], often linking these to active transport and body weight [70]. There are few reports on applications of GPS-derived movement parameters in patient populations. A limited number of studies have shown the reliability and applicability of GPS-derived measures in a sample of patients with peripheral arterial disease [71, 72].

Locomotion speed can already be accurately extracted from IMU readings over shorter distances [73, 74]. Since GPS and IMUs are standard components of contemporary smartphones, there is potential for smartphone-based measurements to replace or complement traditional – usually stopwatch timed – walking tests over short distances (e.g. 10 m). In contrast to GPS devices, IMUs do not require satellites visibility, i.e. they can be used both indoors and outdoors. On the other hand, GPS is the optimal method to determine locations over longer periods of time, and therefore the optimal method to assess life-space mobility.

Though the sources quoted above have demonstrated the potential of GPS- and IMU-derived measures, they still have to be adapted to the new context and tested for their reliability, validity, applicability and acceptance before they can be routinely used by GPs to monitor their patients’ mobility (physical function and life-space). There is also a need to evaluate how GPS- and IMU-derived mobility measures relate to traditional physical function tests.

The overall goal of the MOBITEC-GP project is to provide GPs with the possibility to assess their older patients’ mobility using a collection of different mobility parameters (related to physical function and life space).

The aims of the project are:

An iterative development and evaluation process is required to fulfil all aims; it is illustrated in Fig. 1. The development phases include requirement analyses among GPs, patients and experts. While the first development phase focuses on the app’s measurement properties, the second development phase focusses on user interface design. The app will be specifically designed to create a patient-GP partnership, i.e. the patient collects data of daily functioning and the GP interprets and explains the data to the patient, and plans the necessary measures (such as referral to a comprehensive fall risk assessment or to an exercise programme, home adaptations or provision with adaptive devices) together with the patient.

Specific objectives of Study 1 are:

Specific objectives of Study 2 are:

Study 1 and Study 2 will both be observational. Study 1 will be a validity and reliability study. At baseline (T₀), a battery of tests (visit at study center) as well as a 1-week measurement around participants’ homes will be performed. Cross-sectional analyses of baseline values will be used to evaluate validity of the GPS and IMU-based measurements as well as the inter-instrument agreement between devices of different accuracy levels as well as between different smartphone models. Participants will be assessed again 1 week after the initial assessment (T₁) to evaluate test-retest-reliability of the GPS/IMU measurements. T₁ includes a visit at the study center and a 1-week measurement around participants’ homes.

Study 2 will be an applicability study: Patients equipped with a smartphone (fitted with the new application) will independently perform mobility measurements around their homes for 1 week. The proportion of technically satisfactory GPS/IMU recordings will be determined. Reasons for unsuccessful recordings will be assessed and analysed. Usability and acceptance by GPs and patients will be evaluated.

Both studies target community-dwelling older patients from general practice with multiple chronic conditions, aged 65 or above. The target number of participants for Study 1 is 72, the target number of participants for Study 2 is 60. Participants have to be diagnosed (self-report) with at least two of the following chronic diseases (according to the “Self-Administered Comorbidity Questionnaire” (SCQ) [75, 76]): heart disease, high blood pressure, lung disease, diabetes, ulcer or stomach disease, kidney disease, anemia or other blood disease, cancer, depression, osteoarthritis, degenerative arthritis, back pain and/or rheumatoid arthritis. Participants have to be able to perform a 30-min outdoor walk at their own pace, with or without breaks, with or without walking aid, but without the help of another person (self-report). Persons who are incapable of judgment and persons who are unable to follow procedures or have insufficient knowledge of the German language will be excluded. Participants will have to provide written informed consent.

The recruitment strategy will be twofold. On one hand, participants will be recruited through GP practices. Patients who attend their GP within a given timeframe, are capable of judgement and aged 65 or above will be asked by the GP for their general interest in participating in a study on “mobility”. If they agree they will be informed about the study and assessed for eligibility by a research team member. All eligible patients will be invited to participate. On the other hand, participants will be recruited through presentations about the project in local senior citizen gatherings, individual invitations to persons who expressed interest in participating in studies from our institute, as well as handing out information brochures about the study and individually approaching older adults in settings such as pharmacies, churches, and senior sports groups.

GP practices will be recruited from a network of practices associated with the Center of Primary Health Care, University of Basel.

The performed measurements will only include everyday tasks (such as walking at preferred, habitual pace or rising from a chair), so that they do not involve an increased cardiovascular or musculoskeletal risk compared to everyday activity. During the walking tests, participants will be allowed to take a break at any time.

The new smartphone application will be designed in a way that GPs are only provided with summary measures of physical function and life-space mobility. GPs will not have an insight into the raw movement data or locations that older adults have visited.

The research is carried out in compliance with the Helsinki Declaration. Data generation, transmission, storage and analysis of health related personal data will follow the current Swiss legal requirements for data protection. The project was approved by the Ethics Committee of Northwestern and Central Switzerland (EKNZ) (Reg.-No. 2018–02257).

Participant characteristics (demographics, history of falls, chronic conditions etc.) will be analysed descriptively.