This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2410) Assistive Technologies (115) Innovations and Technology for Physical Activity Education (742) Physical Activity for Older People (179)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26196, first published .
Physical Exercise Program on Fall Prevention Using Technological Interface: Pretest Study

Physical Exercise Program on Fall Prevention Using Technological Interface: Pretest Study

Physical Exercise Program on Fall Prevention Using Technological Interface: Pretest Study

Authors of this article:

Mª Nilza Nogueira1 Author Orcid Image ;   Joana Silva2 Author Orcid Image ;   Isabel Nogueira1 Author Orcid Image ;   Maria Neto Pacheco1 Author Orcid Image ;   Joana Lopes2 Author Orcid Image ;   Fátima Araújo1 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Original Paper

    1Escola Superior de Enfermagem do Porto, Centro de Investigação em Tecnologias e Serviços de Saúde, Porto, Portugal

    2Center for Assistive Information and Communication Solutions, Fraunhofer Portugal Research, Porto, Portugal

    *these authors contributed equally

    Corresponding Author:

    Mª Nilza Nogueira, PhD

    Escola Superior de Enfermagem do Porto

    Centro de Investigação em Tecnologias e Serviços de Saúde

    Escola Superior de Enfermagem do Porto

    Porto, 4200-072

    Portugal

    Phone: 351 225 073 500

    Fax:351 225 096 337

    Email: nilza@esenf.pt


    Background: Prevention of falls among older adults has boosted the development of technological solutions, requiring testing in clinical contexts and robust studies that need prior validation of procedures and data collection tools.

    Objective: The objectives of our study were to test the data collection procedure, train the team, and test the usability of the FallSensing Games app by older adults in a community setting.

    Methods: This study was conducted as a pretest of a future pilot study. Older adults were recruited in a day care center, and several tests were applied. Physical exercise sessions were held using the interactive FallSensing Games app. Nurse training strategies was completed.

    Results: A total of 11 older adults participated. The mean age was 75.08 (SD 3.80) years, mostly female (10/11, 91%) and with low (3-6 years) schooling (10/11, 91%). Clinically, the results show a group of older adults with comorbidities. Cognitive evaluation of the participants through the Mini Mental State Examination showed results with an average score of 25.64 (SD 3.5). Functional capacity assessed using the Lawton Instrumental Activities of Daily Living Scale (overall score from 0-23, with lower scores reflecting worse capacity to perform activities) showed impairment in different instrumental activities of daily living (average score 14.27). The data collection tool proved to enable easy interpretation; however, its structure needed small adjustments to facilitate the data collection process. Despite the length of the questionnaire, its implementation took an average of 21 minutes. For the assessment of the prevalence of fear of falling, the need to add a question was identified. The performance of functional tests under the guidance and presence of rehabilitation nurses ensured the safety of the participants. The interactive games were well accepted by the participants, and the physical exercises allowed data collection on the functionality of the older adults, such as the number of repetitions in the tests, range of movement (angle), duration of the movements, and execution of each cycle. Concerning the training of the nurses, it was crucial that they had experience with the platform, specifically the position of the chair facing the platform, the position of the feet, the posture of participants, and the use of sensors.

    Conclusions: In the future pilot study, the researchers point out the need to design a study with mixed methods (quantitative and qualitative), thus enriching the study results.

    JMIR Form Res 2022;6(6):e26196

    doi:10.2196/26196

    Keywords

    functional tests; fall assessment; exercices; older adults: games; technology 


    In community settings, fall episodes are highly prevalent among the population of older adults [1,2]. Regardless of the severity of the related injuries, the impact on health and quality of life of older adults and their families can be significant since they often trigger and accelerate a cycle of restrictions and barriers leading to the dependency of the older person for activities of daily living [3-5]. Evidence suggests that the frequency of falls increases with age and degree of fragility [6] and that the presence of risk factors directly influences the risk of fall [7,8].

    A structured and standardized screening and assessment of the risk of fall in older adults contribute to its prevention and reduction and are central to the design of the intervention and risk monitoring [7].

    Changes in gait and balance are factors that have been strongly associated with the outcome of fall in older adult population, and rapid tests, such as the 30-Second Chair Stand Test (30CST) [9], the 4-Stage Balance Test (4SBT) [10,11] and the Timed Up and Go (TUG) test [7,12], are recommended for their assessment.

    Evidence-based fall prevention programs have demonstrated a significant reduction in fall risk, falls, and related injuries in older people in a community setting [13,14]. An exercise program with proven effectiveness in preventing falls is the Otago Exercise Program (OEP), designed at the University of Otago Medical School [15-21]. The focus of the OEP is to improve strength and balance with a simple, affordable, and easy home-implemented solution for 12 months, monitored by a health professional through monthly telephone interviews and biannual home visits.

    Recent evidence has strived to integrate technologies into physical exercise programs that have shown a positive effect in adherence and overcoming barriers to exercise, as well as improvements in physical functioning [22,23]. Some technological solutions to facilitate the process of monitoring and fall prevention have already been developed in Portugal, such as the FallSensing Screening and FallSensing Games apps, designed by Fraunhofer Center for Assistive Information and Communication Solutions (AICOS) Portugal.

    The FallSensing Screening app uses inertial measurement units (IMUs) to extract information about the user’s movements, using these data to characterize the movement; it then uses metrics calculated after processing the sensor signal, obtained during the execution of the functional tests performed by the user. The IMU, composed by a triaxial accelerometer, triaxial gyroscope, and triaxial magnetometer, was used to acquire inertial data during the exercises at 50 Hz. Data were transmitted using Bluetooth Low Energy wireless technology to a main computer where the processing occurs. The interactive FallSensing Games app, based on the OEP, aims to improve physical functionality and is also used as a motivator for participants who perform the exercises.

    Therefore, there is an excellent opportunity and a need to develop new user-tailored solutions supported on more robust and valid fall risk predictive models and good clinical practice in fall prevention [6]. Technological solutions need validation in a clinical context, through methodologically robust experimental studies.

    In research, pretesting is an essential stage before the pilot study because it allows for identifying weaknesses in the development of measurement instruments (structure, content, semantics) to determine the potential respondents’ difficulty in interpreting the questions and complexity of the evaluation process. In addition, it enables benchmarking and training procedures and standardizes the modus operandi in data collection, thus contributing to improving the reproducibility and accuracy of measurements [24,25]. This study aims to test the data collection procedure, train the team, and test the usability of the FallSensing Games app by older adults in a community setting.


    Study Design

    A pretest study was performed for the successful implementation of a future larger pilot study. Two research centers were involved in the project, the Nursing School of Porto (ESEP) and the Fraunhofer AICOS Portugal.

    Participants

    Participants were recruited in one of the day centers in western Porto city. For the realization of the FallSensing Games, a minimum of 6 participants was required, but in this study the sample included 11 older adults.

    The inclusion criteria were being aged 65 years or older, living at home, walking independently, not presenting with cognitive impairment according to the Portuguese version of the Mini Mental State Examination (MMSE) [26], not having severe visual or hearing impairment, signing informed consent, and presenting moderate to high risk of falling (assessed through 4 short questions with a dichotomous answer option).

    Participant exclusion criteria included having chronic or acute illness conditions for which exercise is contraindicated; ever having hip or knee surgery or having a history of lower limb fractures in the last 12 months; having participated in physical exercise programs in the last 12 months; having participated in another research study; or having a final MMSE score below 22 (with up to 2 years of school), below 24 (3 to 6 years of school), or below 27 (7 years or more of school).

    Materials

    After selection criteria application, data were collected by the main researchers, and functional testing was performed by two rehabilitation nurses who had received training sessions. In accordance with the best practices recommended for clinical research, the main researcher ensures that their team is trained to implement the different procedures at the different stages of the investigation process. Thus, the training of rehabilitation nurses was performed by the principal investigators. A meeting was held with all the investigators and rehabilitation nurses to present the investigation plan, provide the study dossier (research project, assessment instruments for functional testing instruments, and Otago exercise manual), introduce technologies, explain data collection procedures, and review communication techniques. In the end, there was time for clarification. Subsequently, a training session was held (Figure 1).

    The training procedures took place for a week, covering use of the data collection instrument and performance of functional tests and physical exercises sessions. Among investigators, after consensus meetings, 2 researchers used the data collection instrument, independently and randomly among the participants, in similar spaces. The monitoring of the instrument application time and use of field notes to document difficulties and other observations, such as opinions made by the respondents, were the resources used to assess the applicability of the data collection instrument. Before moving on to the physical exercise sessions, the researchers met to analyze and decide by consensus the questionnaire items identified as needing improvement. Participants performed the functional tests on 3 consecutive days to avoid interfering with the activities previously planned by the day center (Figure 2).

    ‎
    Figure 1. Training procedures.
    View this figure
    ‎
    Figure 2. Study plan.
    View this figure

    Instrument

    The data collection instrument included sociodemographic, clinical, and functional evaluation; fear of falling, and the acceptance of technology. Lower limb strength and muscle resistance were the functional variables assessed through the 30CST; mobility was evaluated by the TUG test (normal step). These functional tests and the 4SBT allowed evaluation of the risk of fall, which was also assessed with the Fall Risk Screening Tool. The functional capacity for activities of daily living was assessed using the Lawton Instrumental Activities of Daily Living Scale (IADL), fear of falling using the Falls Efficacy Scale–International (FES-I), and acceptance of technology by participants and health professionals by the System Usability Scale (SUS). The domains of the instrument are presented below.

    30CST Instrument

    The performance in the 30CST is used as a measure of the strength and muscle resistance of the lower limbs, specifically the extensor muscles of the knee [27,28]. It is a quick test without a dynamometer, training, or special equipment, which allows evaluating the strength of the lower limbs by counting the number of times the individual stands and sits in 30 seconds [9-30]. More strength in the lower limbs is associated with better balance [9-30], and the functional improvement of older adults after a fall prevention program will be manifested by a greater number of repetitions in 30 seconds in the posttest assessment [14]. The results have shown good psychometric qualities [9-30].

    TUG Test (Normal Step)

    Since mobility assessment of older adults is a central component in the geriatric assessment [31], the TUG test is proposed to evaluate the clinical utility of the timed stand and walk test. This test measures in seconds the time an individual takes to stand from a chair, walk a distance of 3 meters, return, and sit back in the chair. These authors reported that time spent on the TUG test performance was related to scores on the Berg Balance Scale (r=–0.72) and the walking speed (r=–0.55) and Barthel Activities of Daily Living Index (r=–0.51) scores. Individuals who completed the test in less than 20 seconds were independent in transferring, and individuals who completed the test in more than 30 seconds tended to be dependent on this task.

    The TUG test has been widely referred to and used [12] as a screening test to assess the risk of fall in older adults in community settings, namely through the guideline of the American Geriatric Society and British Geriatric Society and in the US Centers for Disease Control and Prevention (Stopping Elderly Accidents, Deaths, and Injuries [STEADI initiative]).

    A prospective design conducted to evaluate the predictive ability of the TUG test for future falls and estimate the best cutoff point of the test pointed to 12.6 seconds, with the corresponding values of sensitivity (30.5%), specificity (89.5%), positive predictive value (46.2%), and negative predictive value (81.4%) [12]. The researchers who conducted the study emphasized the high specificity (89.5%) and high negative predictive value (81.4%) to a cutoff point of 12.6 seconds as a support for the clinical utility of this test in older adults at high risk of falling. Researchers reported that after a fall prevention program, performing the test in less time is indicative of improvement [14]. In Portugal, this test has been used in several studies [32,33].

    4SBT Instrument

    The balance tests were conceptually developed to track balance impairments [11,34,35] placing the older adults at risk of falling [10,11]. More specifically, the 4SBT is used to track impairment in the static balance of older adults. Several authors have found the test with an excellent performance in test-retest reliability (r=.97) and interrater reliability (Κ=.92) [10,36]. The success of fall prevention programs is measured by comparing the positions achieved in 10 seconds in the pre- and postprogram evaluation [14]. The final score will be the number of positions successfully completed for 10 seconds without losing balance. The older adults who cannot maintain position 3 for 10 seconds have a high risk of falling [37].

    IADL Instrument

    The IADL assesses the level of independence of older adults in performing activities of daily living, which integrate day-to-day tasks such as using the telephone, shopping, preparing food, housekeeping, washing clothes, using transport, preparing medication, and handle finances. It is an easy-to-administer tool that can be used with older adults in a community and hospital setting [38-40].

    In this study, the Portuguese version [41], which uses the same items as the original version but applies a polychotomous score (0, 1, 2, 3, or 4) instead of the original dichotomous score (0 and 1), was used, allowing for a better description of a person’s ability to perform the tasks, giving each response option a different score. The total score of the scale varies from 0 to 23, with a lower score corresponding to worse performance. In the validation study, the instrument showed good metric qualities to be applied in a community setting.

    FES-I Instrument

    The fear of falling among older adults is an expressive problem and highly relevant because it is associated with adverse effects on mobility and quality of life [42-44]. One of the instruments used to evaluate this construct is the FES-I [45]. Its adaptation to different languages and cultural contexts (following the protocol recommended by the Prevention of Falls Network Europe Group), has allowed the instrument to be widely applied and the results compared in different populations and contexts. The FES-I version is an instrument that incorporates some daily activities that are a little more complex than those of the original version and others more focused on the social life of older adults as a way to overcome some weaknesses identified in the original version. For each of the 16 items, the answer option is based on a 4-point Likert scale (1=not at all worried; 2=somewhat worried; 3=moderately worried; 4=very worried). The instrument has shown validity, reliability, and comparability across cultures, so it is recommended for research practice and the clinical context, namely in fall prevention programs for older adults population [46].

    In this research, the FES-I version validated for the Portuguese population [47] showed excellent internal consistency (α=.98) and test-retest reliability (intraclass correlation coefficient 2.1=0.999). Concurrent validity, assessed using the Activity-Specific Balance Confidence Scale, presented results indicative of good concurrent validity (rs=–0.85; P<.001). Considering the global results, the authors consider the Portuguese version of the FES-I a reliable and valid measure to assess the fear of falling among the Portuguese older adult population living in the community.

    SUS Instrument

    To validate the acceptance of the technology by participants and health professionals, the responses of the SUS was analyzed. This rapid test evaluates the usability of a certain product or service [48]. This test has several features that provide a good assessment of the overall usability, such as the flexibility to evaluate interface technologies, interactive voice response systems [49], hardware platforms used in more traditional computer interfaces, and websites. Ease and speed of use (by both participants and system administrators), ease of operation of scoring, and the free access characteristic are also advantages.

    The original SUS instrument consists of 10 statements that are scored on a 5-point Likert scale (1=strongly disagree to 5=strongly agree) [48]. The final score can vary from 0 to 100 points, with a better score indicating better usability [49], and the final score needs to be considered following the instructions of the original instrument because statements switch between positive and negative. A study conducted at the national level performed a psychometric analysis of the tool intending to translate, culturally adapt, and contribute to the validation of the European Portuguese version of the SUS [50].

    Procedures

    Technology Platform

    The technology platform uses mobile app inertial sensors to extract information on movement performed by the participant and related characterization. This platform measures pressure distribution at 50 Hz and comprises 1600 pressure sensors (10 mm × 10 mm) with maximum value of 100 N/sensor. The size of the active area of the pressure platform is a square matrix of 40 cm × 40 cm. Voltage data are converted with an 8-bit A/D converter and transmitted via USB to the main computer. The risk of fall is then determined from parameters calculated after processing the signal from the inertial sensors during the execution of functional tests such as walking, sitting, and standing.

    Games

    The interactive FallSensing Games app is based on the OEP and the use of inertial sensors to monitor the movements performed by the participants during these exercises. To interact with the characters and achieve the objectives of each game, participants must perform the suggested movements correctly. Monitoring the movements of participants in each game allows us to assess the evolution of physical capacity and extract parameters related to functional capacity.

    The FallSensing Games app comprises 3 minigames, with each minigame comprising 2 to 3 exercises from the OEP. The composition of the minigames is as follows [29]:

    • Minigame 1 includes knee bends and sit-to-stand exercises monitored with a sensor on the thigh.
    • Minigame 2 includes lateral hip abduction (side hip), frontal knee extension (front knee), and backwards knee flexion (back knee) monitored with the sensor on the ankle.
    • Minigame 3 includes calf and toe raises monitored with sensors on the top of the foot.
    Physical Exercise Session

    The Otago physical exercise session, supported by interactive games, was implemented by the rehabilitation nurses in the day centers. Participants were divided into 2 groups. In the physical exercise session, after the demonstration, the rehabilitation nurse started with the warm-up exercises suggested by the OEP followed by exercises to strengthen the lower limbs and improve balance and stability and finally the relaxation phase, with stretching exercises. For the implementation of the physical exercise session, we used the following material resources: (1) room with free space (at health centers), (2) computer and television/projector, (3) wearables (IMUs) with loaders and fixing tapes, and (4) a pressure platform.

    Ethics Approval

    The pretest study was approved by the Health Ethical Committee from ESEP (annex 5 to document no. 4/2019). All participants were informed and provided informed consent in duplicate (one copy for participant and one copy to investigator) before enrolling. Participants were informed about the confidential information protection, the right to study withdrawal, data anonymity, and the likelihood of study publication.

    Data Analysis

    SPSS (version 26.0, IBM Corp) software was used for statistical analysis. The univariate descriptive analysis was applied to describe data supported on measures of central tendency and dispersion.


    Descriptive Information

    The pretest was conducted on 11 participants with a mean age of 75.08 (SD 3.80) years, mostly female (10/11, 91%) and with low (3-6 years) schooling (10/11, 91%). Clinically, the results show a group of older adults with comorbidities who portray the epidemiological profile of chronic disease, with high expressiveness of hypertension, osteoarticular disease, and urinary incontinence. In this sample, despite the high prevalence of osteoarticular disease, only 2 older adults used walking aids. More than half (6/11, 55%) of participants reported depression. This clinical pattern was accompanied by drug regimens that integrate mostly 4 or more drugs (10/11, 91%). Balance impairment or difficulty in walking was referred to by 64% (7/11) of participants. More than half (6/11, 55%) presented a high risk of falling, due to recurrent falls in the last 12 months.

    Cognitive evaluation of the participants using the MMSE showed results with an average score of 25.64 (SD 3.5), consistent with a mild degree of impairment for participants with a low level of education. Functional capacity assessed using the IADL (overall score varying from 0 to 23, with lower scores reflecting worse capacity to perform activities) showed impairment in different activities of daily living (average score 14.27). The results of the descriptive analysis for sociodemographic and clinical variables are presented in Table 1.

    Concerning the fear of falling, the results showed that the activities in which participants reported higher levels of fear (response options: 3=moderately concerned and 4=very concerned) were walking on slippery surfaces (7/11), going up and down stairs (6/11), and walking on uneven surfaces and walking up and down slopes (5/11 for both). In the self-care dressing/undressing, shopping, and walking in the neighborhood, 4 older adults were identified with the response option 3 or 4 on the Likert scale. The response option equal to 2 (a little worried) was expressed by 10 out of 11 participants for a variable number between 1 to 6 activities.

    Table 1. Participant characteristics.
    CharacteristicsValue
    Age (years), mean (SD)75.09 (3.80)

    		65-74, n (%)3 (27)

    		75-84, n (%)8 (73)
    Gender (female), n (%)10 (91)
    Marital status, n (%)

    		Married2 (18)

    		Single2 (18)

    		Divorced2 (18)

    		Widowed5 (45)
    Education (years), n (%)

    		0-21 (9)

    		3-610 (91)
    Comorbid health conditions (yes), n (%)

    		Arterial hypertension8 (73)

    		Osteoarticular disease8 (73)

    		Urinary incontinence8 (73)

    		Depression6 (55)

    		Vertigo syndrome6 (55)

    		Diabetes mellitus5 (45)

    		Vision changes4 (36)
    Daily medication consumption (≥4), n (%)10 (91)
    MMSEa score, mean (SD)25.64 (3.50)
    IADLb, mean (SD)14.27 (7.14)
    Walking difficulties/balance compromised (yes), n (%)7 (64)
    Walking aids (yes), n (%)2 (18)
    Falls (yes), n (%)

    		High risk6 (55)

    		History of falls (last 12 months)6 (55)

    		Recurrent falls6 (55)

    		Indoor falls4 (67)

    		Health care need after falls2 (33)

    aMMSE: Mini Mental State Examination

    bIADL: Lawton Instrumental Activities of Daily Living Scale

    Data Collection Procedure

    In general, at the interview stage no difficulties of interpretation were identified that could make it difficult to answer the questions of the data collection instrument; however, for the FES-I assessment both researchers needed to frequently recall the Likert scale in use. In the consensus meetings, it was found in the field notes of the researchers that the behavior of the older adults in the assessment of fear of falling are indicative of increased difficulties in interpreting the request and choosing the answer option. This fact showed the need to evaluate this construct in a simpler way that also allowed distinguishing the participants regarding the level of fear of falling. After research, it was decided to include a single-item question “Are you afraid of falling?” with the same ordinal answer option (not at all worried, a little worried, moderately worried, very worried) in the definitive questionnaire for the future pilot study. According to some authors, there is not enough evidence that more complex measures consisting of several items better assess the fear of falling into this population range than single item questions [51-53].

    In the 30CST test, an average of 4.9 repetitions was obtained. The average time of the TUG test was 21.9 seconds, with 12.6 seconds being used as a cutoff point, less time in the test performance means better functional condition. Finally, in the performance of the 4SBT, all participants were able to perform positions 1 and 2 with their eyes open, but only 55% (xx/xx) were able to achieve position 3 (Table 2).

    During the functional tests, the participants presented difficulties in the execution of the instruction given for the 4SBT test, despite the previous demonstration of the 4 foot positions performed by both rehabilitation nurses under the supervision of the investigators (Figure 3).

    During the 4SBT functional test, the older adults showed a behavior of constantly searching for support in the surrounding environment (people, walls, chairs). This fact is reported in the literature on falls in elderly populations as indicative of fear of falling or low perception of self-efficacy to perform the task.

    The average time to complete the data collection tool—sociodemographic data, clinical and drug consumption history, IADL, and FES-I—was 21 (SD 2.62) minutes. Before starting data collection, each of the researchers reminded the participants of the study objectives and the possibility of being able at any time to express their willingness to withdraw without any negative implication.

    Table 2. Functional test results.
    TestsValueMinimum-maximum
    30CSTa, mean (SD)4.9 (3.315)0-11
    TUGb

    		Duration (seconds), mean (SD)21.90 (5.74)13.96-31.38

    		≥12.6 seconds, n (%)100—c
    4SBTd (4 foot positions), n (%)

    		Position 1100—

    		Position 2100—

    		Position 355—

    		Position 49—

    a30CST: 30-Second Chair Stand Test.

    bTUG: Timed Up and Go.

    cNot applicable.

    d4SBT: 4-Stage Balance Test.

    ‎
    Figure 3. Number of foot positions in 4-Stage Balance Test, with eyes open.
    View this figure

    Train the Team

    The training of nurses was meant to standardize the application of functional tests, guidance in games and physical exercises, and interactions with the older adults through appropriate communication techniques and security measures. Regarding the use of technology, the training of nurses allowed validation of the correct use and placement of sensors as well as the correct use of the platform.

    Functional Tests Application Procedure

    Before the functional tests application procedure, there was a need to establish a relationship of trust between the nurses and participants. The tests were explained and then demonstrated. Special attention was given to the nurses’ position toward older adults, tone of voice, rhythm of explanation, and nonverbal communication.

    In particular, for the 4SBT it was important to measure the position of the chair facing the platform, the position of the feet, and the posture of older adults on the platform. For both tests (4SBT and TUG), the position of the nurse beside older adults during the test execution proved to ensure the safety of the participants. To mark the path of the execution of the TUG test, colored ribbons were placed on the floor, which were identified by the participants as providing good assistance (Figure 4).

    Regarding the use of technological devices, it was necessary to check the position of the sensors, both in the anatomical location and in their local adjustment (avoiding discomfort for older adults or coming loose in order to obtain correct readings and avoid repeating the test several times (Figure 5).

    ‎
    Figure 4. Timed Up and Go test execution path, marked with colored ribbons.
    View this figure
    ‎
    Figure 5. Anatomical position of sensors and foot positions inside the platform.
    View this figure
    Physical Exercise Session

    The physical exercise session using the FallSensing Games app led to adjustments in its implementation: (1) maintenance of a minimum distance between the participants’ chairs to avoid touching each other during the shoulder abduction exercise, (2) synchronization between the material resources (TV/computer) and the start of the warm-up exercises, (3) presence of the nurse near the participants to adjust the correct use of the elastic bands, and (4) adaptation of the nurses’ paralanguage (tone of voice) to the sound volume of the games.

    Minigames

    For each exercise composing 1 of the 3 minigames, the wearable inertial sensors allow us to extract 3 relevant metrics, such as the range of motion angle (angle), range of motion duration (cycle time), and number of repetitions (nr_cycles). Considering that each exercise requires a specific number of repetitions defined according to the OEP, each of these metrics will be computed for each repetition. For example, if we consider the knee bending exercise, each person should perform 10 repetitions of the exercise, allowing us to compute the angle and duration of each repetition and also count the number of performed repetitions. Given that each participant performed each 1 of the 3 minigames, we have computed the mean of each metric for each participant. Table 3 presents the values for each metric averaged for all the participants and its standard deviation.

    For minigame 1, each participant performed on average 6 repetitions of sit-to-stand and 23 repetitions of the knee-bending exercise. For minigame 2, each participant performed on average 25 repetitions of knee extension, 25 repetitions of knee flexion, and only 13 repetitions of hip abduction (side hip) exercise. For minigame 3, each participant performed on average 15 repetitions of calf raises and 13 repetitions of toe raises.

    In sum, each participant performed on average more repetitions of each exercise than suggested in the OEP due to the gamification of these exercises in the minigames, which requested the participants to perform more repetitions to accomplish a higher game score. This can be seen as a positive effect of the gamification of the Otago exercises. Another relevant outcome is the retrieval of range of motion–related metrics, as the angle and duration of movements, which can only be quantified when using wearable sensors as opposed to traditional observational programs.

    Table 3. Minigame metrics.
    Game number and inertial sensor metricValue, mean (SD)
    Minigame 1

    		Sit_to_stand_angle64.43 (22.29)

    		Sit_to_stand_cycle_time3.20 (1.46)

    		Sit_to_stand_nr_cycles6.33 (4.54)

    		Knee_bends_angle35.67 (14.61)

    		Knee_bends_cycle_time2.19 (0.82)

    		Knee_bends_nr_cycles22.58 (14.44)
    Minigame 2

    		Knee_extension_angle80.02 (21.80)

    		Knee_extension_cycle_time2.02 (1.37)

    		Knee_extension_nr_cycles25.00 (10.39)

    		Knee_flexion_angle80.98 (17.84)

    		Knee_flexion_cycle_time1.90 (0.98)

    		Knee_flexion_nr_cycles24.56 (10.74)

    		Side_hip_angle50.03 (26.11)

    		Side_hip_cycle_time2.45 (1.11)

    		Side_hip_nr_cycles13.00 (5.02)
    Minigame 3

    		Calf_raises_angle35.72 (22.92)

    		Calf_raises_cycle_time3.46 (4.72)

    		Calf_raises_nr_cycles14.75 (9.85)

    		Toe_raises_angle29.48 (24.21)

    		Toe_raises_cycle_time2.64 (1.86)

    		Toe_raises_nr_cycles13.42 (5.98)

    Test the Usability of the FallSensing Games App

    As for participant satisfaction using the SUS, the results showed that out of the participants who responded, 50% (5/10) assessed the usability of the technology as acceptable, 30% (3/10) expressed good satisfaction, and 20% (2/10) considered the usability of the technology as problematic. One participant did not respond.

    In addition to this quantitative analysis, field notes were collected on satisfaction expressed by the participants, who voiced their satisfaction with the games, participation in team games, and repetition of the activity’s animated penguins. Statements from participants included “I had never made a game looking at penguins,” “I didn’t realize that time was passing,” “I even forgot the pains,” “I even laughed a little bit,” and “You need to come here more often.” During the stay at the day center, we observed the acceptance of the games, both for the ease of integration in the activities of older adults and for the ease with which older adult followed the games.


    Principal Findings

    Test the Data Collection Procedure

    From the nurses’ perspective, due to the speed and consistency of the participant answers, the data collection tool proved to enable an easy interpretation. However, its structure needed small adjustments to facilitate the data collection process. Despite the length of the questionnaire, its implementation took an average of 21 minutes. For the assessment of the fear of falling, the need to add a question was identified to clarify whether the participant was afraid of falling. The performance of functional tests by the participants under the guidance and presence of rehabilitation nurses ensured the safety of the participants.

    Train the Team

    Concerning the training of the nurses, it was crucial that they had experience with the platform, specifically the position of the chair facing the platform, the position of the feet, and the posture of the participants on the platform, which allowed adjustments to minimize errors in the functional test assessment. At the same time, the use of sensors and their anatomical position and adjustment allowed us to understand that the way to hold them needs to be improved.

    Test the Usability of the FallSensing Games App

    Regarding the games, we can point out 2 aspects. On the one hand, each participant performed on average more repetitions of each exercise than suggested in the OEP to achieve a higher game score. On the other hand, obtaining metrics related to range of motion, such as the angle and duration of movements, was only possible with the use of wearable sensors. The easy integration of games in the activities of the older adult care center and the ease of the older adults in following the games corroborates the results presented by previous research [29].

    Limitations

    As limitations of the study, we highlight the (1) small sample size; (2) absence of an observation grid of the participants’ behavior during the performance of the functional tests and games, which could, in a future pilot study, reflect the realism of the situation under study; (3) spontaneous appreciation of the participants, expressed by the contentment and appreciation of the moments spent together, showing the researchers the need to collect this experience in a planned and rigorous way, namely the feelings and emotions of the participants; and (4) concern to prepare the team of nurses for the application of functional tests, use of the platform and sensors, and physical exercise session with the games led to some aspects being neglected, namely the possibility of incorporating qualitative component into the study.

    Therefore, in the future pilot study, the researchers point out the need to design a study with mixed methods (quantitative and qualitative), thus enriching the study results. The researchers, intend to use qualitative methods, such as focus group, for the participants, which can enrich the exchange of experiences during the games and nonparticipant observation, with the use of an observation grid, which can favor the collection of information on the correct execution of the Otago exercises.

    Regarding the sample size, the recruitment can be improved by incorporating more day centers and a longer period for data collection.

    Despite the limitations of the pretest study and results, this study aims to contribute to the practice of professionals in clinical and research contexts, given the scarcity of information on this relevant stage in experimental/quasi-experimental studies.

    Acknowledgments

    The National Network of Knowledge Transfer on Medical Devices is an initiative cofunded by Programa Operacional Competitividade e Internacionalização, Portugal 2020, and the European Union through the Fundo Europeu de Desenvolvimento Regional.

    Authors' Contributions

    NN and FA contributed to the design of the study protocol. NN and FA contributed to the drafting the manuscript. NN, FA, and JS contributed with critical revisions to the paper for important intellectual content. NN, FA and JS obtained the funding. JS described the technological solutions used in the study. NN, FA, IN and MP contributed to the definitions of participant recruitment and ethical considerations.

    Conflicts of Interest

    None declared.

    1. Brito TA, Coqueiro RDS, Fernandes MH, de Jesus CS. Determinants of falls in community-dwelling elderly: hierarchical analysis. Public Health Nurs 2014;31(4):290-297. [CrossRef] [Medline]
    2. Stevens JA, Mahoney JE, Ehrenreich H. Circumstances and outcomes of falls among high risk community-dwelling older adults. Inj Epidemiol 2014 Dec;1(1):5 [FREE Full text] [CrossRef] [Medline]
    3. Schoene D, Heller C, Aung YN, Sieber CC, Kemmler W, Freiberger E. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging 2019;14:701-719 [FREE Full text] [CrossRef] [Medline]
    4. Thiem U, Klaaßen-Mielke R, Trampisch U, Moschny A, Pientka L, Hinrichs T. Falls and EQ-5D rated quality of life in community-dwelling seniors with concurrent chronic diseases: a cross-sectional study. Health Qual Life Outcomes 2014 Jan 08;12:2 [FREE Full text] [CrossRef] [Medline]
    5. Vitorino LM, Teixeira CAB, Boas ELV, Pereira RL, Santos NOD, Rozendo CA. Fear of falling in older adults living at home: associated factors. Rev Esc Enferm USP 2017 Apr 10;51:e03215 [FREE Full text] [CrossRef] [Medline]
    6. WHO Global Report on Falls Prevention in Older Age. Geneva: World Health Organization; 2007.   URL: https:/​/extranet.​who.int/​agefriendlyworld/​wp-content/​uploads/​2014/​06/​WHo-Global-report-on-falls-prevention-in-older-age.​pdf [accessed 2022-06-01]
    7. Panel on Prevention of Falls in Older Persons, American Geriatrics Society, and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc 2011 Jan;59(1):148-157. [CrossRef] [Medline]
    8. Kvelde T, Lord SR, Close JCT, Reppermund S, Kochan NA, Sachdev P, et al. Depressive symptoms increase fall risk in older people, independent of antidepressant use, and reduced executive and physical functioning. Arch Gerontol Geriatr 2015;60(1):190-195. [CrossRef] [Medline]
    9. Cho KH, Bok SK, Kim Y, Hwang SL. Effect of lower limb strength on falls and balance of the elderly. Ann Rehabil Med 2012 Jun;36(3):386-393 [FREE Full text] [CrossRef] [Medline]
    10. Murphy MA, Olson SL, Protas EJ, Overby AR. Screening for falls in community-dwelling elderly. J Aging Phys Activity 2003 Jan;11(1):66-80. [CrossRef]
    11. Thomas JC, Odonkor C, Griffith L, Holt N, Percac-Lima S, Leveille S, et al. Reconceptualizing balance: attributes associated with balance performance. Exp Gerontol 2014 Sep;57:218-223 [FREE Full text] [CrossRef] [Medline]
    12. Kojima G, Masud T, Kendrick D, Morris R, Gawler S, Treml J, et al. Does the timed up and go test predict future falls among British community-dwelling older people? Prospective cohort study nested within a randomised controlled trial. BMC Geriatr 2015 Apr 03;15(1):38 [FREE Full text] [CrossRef] [Medline]
    13. Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 2012 Sep 12(9):CD007146. [CrossRef] [Medline]
    14. Renfro M, Bainbridge DB, Smith ML. Validation of evidence-based fall prevention programs for adults with intellectual and/or developmental disorders: a modified Otago exercise program. Front Public Health 2016;4:261 [FREE Full text] [CrossRef] [Medline]
    15. Campbell AJ, Robertson MC, Gardner MM, Norton RN, Buchner DM. Falls prevention over 2 years: a randomized controlled trial in women 80 years and older. Age Ageing 1999 Oct;28(6):513-518. [CrossRef] [Medline]
    16. Campbell AJ, Robertson MC, Gardner MM, Norton RN, Tilyard MW, Buchner DM. Randomised controlled trial of a general practice programme of home-based exercise to prevent falls in elderly women. BMJ 1997 Oct 25;315(7115):1065-1069 [FREE Full text] [CrossRef] [Medline]
    17. Campbell AJ, Robertson MC, La Grow SJ, Kerse NM, Sanderson GF, Jacobs RJ, et al. Randomised controlled trial of prevention of falls in people aged > or =75 with severe visual impairment: the VIP trial. BMJ 2005 Oct 08;331(7520):817 [FREE Full text] [CrossRef] [Medline]
    18. Robertson MC, Devlin N, Gardner MM, Campbell AJ. Effectiveness and economic evaluation of a nurse delivered home exercise programme to prevent falls: randomised controlled trial. BMJ 2001 Mar 24;322(7288):697-701 [FREE Full text] [CrossRef] [Medline]
    19. Robertson MC, Gardner MM, Devlin N, McGee R, Campbell AJ. Effectiveness and economic evaluation of a nurse delivered home exercise programme to prevent falls: controlled trial in multiple centres. BMJ 2001 Mar 24;322(7288):701-714 [FREE Full text] [CrossRef] [Medline]
    20. Stevens J, Burns E. A CDC compendium of effective fall interventions: what works for community-dwelling older adults. Atlanta: Centers for Disease Control and Prevention; 2015.   URL: https://www.cdc.gov/homeandrecreationalsafety/pdf/falls/cdc_falls_compendium-2015-a.pdf [accessed 2021-07-19]
    21. Thomas S, Mackintosh S, Halbert J. Does the 'Otago exercise programme' reduce mortality and falls in older adults?: a systematic review and meta-analysis. Age Ageing 2010 Nov;39(6):681-687. [CrossRef] [Medline]
    22. Jessen JD, Lund HH. Study protocol: effect of playful training on functional abilities of older adults: a randomized controlled trial. BMC Geriatr 2017 Jan 19;17(1):27 [FREE Full text] [CrossRef] [Medline]
    23. Valenzuela T, Okubo Y, Woodbury A, Lord SR, Delbaere K. Adherence to technology-based exercise programs in older adults: a systematic review. J Geriatr Phys Ther 2016 Jun 29;41(1):49-69. [CrossRef] [Medline]
    24. Hilton CE. The importance of pretesting questionnaires: a field research example of cognitive pretesting the Exercise referral Quality of Life Scale (ER-QLS). Int J Soc Res Methodol 2015 Oct 07;20(1):21-34. [CrossRef]
    25. Survey Research Center. Guidelines for Best Practice in Cross-Cultural Surveys, 4th Edition. Ann Arbor: Institute for Social Research, University of Michigan; 2016.
    26. Morgado J, Rocha C, Maruta C, Guerreiro M, Martins I. New normative values of Mini Mental States Examination. Sinapse 2009;9(2):10-16.
    27. Bohannon RW, Bubela DJ, Magasi SR, Wang Y, Gershon RC. Sit-to-stand test: performance and determinants across the age-span. Isokinet Exerc Sci 2010;18(4):235-240 [FREE Full text] [CrossRef] [Medline]
    28. Wretenberg P, Arborelius UP. Power and work produced in different leg muscle groups when rising from a chair. Eur J Appl Physiol Occup Physiol 1994;68(5):413-417. [CrossRef] [Medline]
    29. Silva J, Oliveira E, Moreira D, Nunes F, Caic M, Madureira J, et al. Design and evaluation of a fall prevention multiplayer game for senior care centres. In: Clua E, Roque L, Lugmayr A, Tuomi P, editors. Entertainment Computing. Cham: Springer; 2018:103-114.
    30. Jones CJ, Rikli RE, Beam WC. A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Res Q Exerc Sport 1999 Jun;70(2):113-119. [CrossRef] [Medline]
    31. Podsiadlo D, Richardson S. J Am Geriatr Soc 1991 Feb;39(2):142-148. [CrossRef] [Medline]
    32. Branco PS. Determinação dos Pontos de Corte para Elevado Risco de Queda e Mobilidade Normal da Versão Portuguesa da Activities-Specific Balance Confidence (ABC) Scale. Revista da Sociedade Portuguesa de Medicina Física e de Reabilitação 2013;24(2):12-17.
    33. Preto L, Mendes E, Novo A, Santos A. Mudanças no desempenho funcional e composição corporal em idosos institucionalizados: um estudo longitudinal. In: Nery de Souza D, Rua M, editors. Cuidadores Informais de Pessoas Idosas: Caminhos de Mudança. Aveiro: Universidade Aveiro Editora; 2013:331-335.
    34. Bean JF, Kiely DK, LaRose S, Leveille SG. Which impairments are most associated with high mobility performance in older adults? Implications for a rehabilitation prescription. Arch Phys Med Rehabil 2008 Dec;89(12):2278-2284. [CrossRef] [Medline]
    35. Jette AM. Disablement outcomes in geriatric rehabilitation. Med Care 1997 Jun;35(6 Suppl):JS28-JS44. [CrossRef] [Medline]
    36. Winograd CH, Lemsky CM, Nevitt MC, Nordstrom TM, Stewart AL, Miller CJ, et al. Development of a physical performance and mobility examination. J Am Geriatr Soc 1994 Jul;42(7):743-749. [CrossRef] [Medline]
    37. National Center for Injury Prevention and Control. Tools to Implement the Otago Exercice Program: A Program to Reduce Falls, First edition. Atlanta: Division of Unintentional Injury Prevention, Centers for Disease Control and Prevention; 2017.   URL: https://www.med.unc.edu/aging/cgwep/files/2018/09/ImplementationGuideforPT.pdf [accessed 2022-06-01]
    38. Graf C. The Lawton Instrumental Activities of Daily Living (IADL) Scale. Best Practices in Nursing Care to Older Adults. 2017.   URL: https://dementiasherpa.com/wp-content/uploads/2017/09/Lawton-IADL-scale.pdf [accessed 2021-07-19]
    39. Mlinac ME, Feng MC. Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsychol 2016 Sep;31(6):506-516. [CrossRef] [Medline]
    40. Peng L, Lu W, Liang C, Chou M, Chung C, Tsai S, Taiwan Stroke Postacute Care (PAC) Study Group. Functional outcomes, subsequent healthcare utilization, and mortality of stroke postacute care patients in Taiwan: a nationwide propensity score-matched study. J Am Med Dir Assoc 2017 Nov 01;18(11):990.e7-990.e12. [CrossRef] [Medline]
    41. Araújo F, Ribeiro J, Oliveira A, Pinto C, Martins T. 2008 Presented at: Actas do 7th Congresso Nacional de Psicologia da Saúde 2008: Intervenção em Psicologia e Saúde; 2008; Porto. [CrossRef]
    42. Cruz DTD, Duque RO, Leite ICG. Prevalence of fear of falling, in a sample of elderly adults in the community. Rev Bras Geriatr Gerontol 2017 May;20(3):309-318. [CrossRef]
    43. Delbaere K, Close JCT, Mikolaizak AS, Sachdev PS, Brodaty H, Lord SR. The Falls Efficacy Scale International (FES-I). A comprehensive longitudinal validation study. Age Ageing 2010 Mar;39(2):210-216. [CrossRef] [Medline]
    44. Scheffer AC, Schuurmans MJ, van Dijk N, van der Hooft T, de Rooij SE. Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 2008 Jan;37(1):19-24. [CrossRef] [Medline]
    45. Yardley L, Beyer N, Hauer K, Kempen G, Piot-Ziegler C, Todd C. Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing 2005 Nov;34(6):614-619 [FREE Full text] [CrossRef] [Medline]
    46. Marques-Vieira CMA, Sousa LMM, Severino S, Sousa L, Caldeira S. Cross-cultural validation of the falls efficacy scale international in elderly: systematic literature review. J Clin Gerontol Geriatr 2016 Sep;7(3):72-76. [CrossRef]
    47. Figueiredo D, Santos S. Cross-cultural validation of the Falls Efficacy Scale-International (FES-I) in Portuguese community-dwelling older adults. Arch Gerontol Geriatr 2017 Jan;68:168-173. [CrossRef] [Medline]
    48. Brooke J. SUS: a "quick and dirty" usability scale. In: Usability Evaluation in Industry. London: Taylor & Francis; 1996:189-194.
    49. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the System Usability Scale. Int J Human Comput Interact 2008 Jul 30;24(6):574-594. [CrossRef]
    50. Martins AI, Rosa AF, Queirós A, Silva A, Rocha NP. European Portuguese validation of the System Usability Scale (SUS). Procedia Comput Sci 2015;67:293-300. [CrossRef]
    51. Belloni G, Büla C, Santos-Eggimann B, Henchoz Y, Seematter-Bagnoud L. A single question as a screening tool to assess fear of falling in young-old community-dwelling persons. J Am Med Dir Assoc 2020 Sep;21(9):1295-1301. [CrossRef] [Medline]
    52. Murphy SL, Dubin JA, Gill TM. The development of fear of falling among community-living older women: predisposing factors and subsequent fall events. J Gerontol A Biol Sci Med Sci 2003 Oct;58(10):M943-M947 [FREE Full text] [CrossRef] [Medline]
    53. Zijlstra GAR, van Haastregt JCM, van Eijk JTM, van Rossum E, Stalenhoef PA, Kempen GIJM. Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people. Age Ageing 2007 May;36(3):304-309. [CrossRef] [Medline]

    ‎
    30CST: 30-Second Chair Stand Test
    4SBT: 4-Stage Balance Test
    AICOS: Center for Assistive Information and Communication Solutions
    ESEP: Escola Superior de Enfermagem do Porto
    FES-I: Falls Efficacy Scale–International
    IADL: Lawton Instrumental Activities of Daily Living Scale
    IMU: inertial measurement unit
    MMSE: Mini Mental State Examination
    OEP: Otago Exercise Program
    STEADI: Stopping Elderly Accidents, Deaths, and Injuries
    SUS: System Usability Scale
    TUG: Timed Up and Go

    Edited by A Mavragani; submitted 01.12.20; peer-reviewed by R Boumans, K Liu, Y Cao, M Erdt; comments to author 18.08.21; revised version received 04.11.21; accepted 22.05.22; published 29.06.22

    Copyright

    ©Mª Nilza Nogueira, Joana Silva, Isabel Nogueira, Maria Neto Pacheco, Joana Lopes, Fátima Araújo. Originally published in JMIR Formative Research (https://formative.jmir.org), 29.06.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.