Abstract
Introduction:
Concern about falling is a prevalent worry among community-dwelling older adults and may contribute to a decline in physical and mental health. This study aimed to examine the association between mobility performance and concern about falling.
Methods:
Older adults aged 65 years and older, with Mini-Mental State Examination score (MMSE) ⩾24, and ambulatory (with or without the assistive device) were included. Concern about falling was evaluated with Falls Efficacy Scale-International (FES-I) scores. Participants with high concern about falling were identified using the cutoff of FES-I⩾23. Participants’ motor capacity was assessed in standardized walking tests under single- and dual-task conditions. Participants’ mobility performance was measured based on a 48-hour trunk accelerometry signal from a wearable pendant sensor.
Results:
No significant differences were observed at participant characteristics across groups with different levels of concern about falling (low: N = 64, age = 76.3±7.2 years, female = 46%; high: N = 59, age = 79.3±9.1 years, female = 47%), after propensity matching with body mass index, age, depression, and cognition. With adjustment of motor capacity (stride velocity and stride length under single- and dual-task walking conditions), participants with high concern about falling had significantly poorer mobility performance than those with low concern about falling, including lower walking quantity (walking bouts, steps and time per day, and walking bout average, walking bout variability, and longest walking bout, p ⩽ 0.013), and poorer daily-life gait (stride velocity and gait variability, p ⩽ 0.023), and poorer walking quality (frontal gait symmetry, and trunk acceleration and velocity intensity, p ⩽ 0.041). The selected mobility performance metrics (daily steps and frontal gait symmetry) could significantly contribute to identifying older adults with high concern about falling (p ⩽ 0.042), having better model performance (p = 0.036) than only walking quantity (daily steps) with adjustment of confounding effects from the motor capacity (stride length under dual-task walking condition).
Conclusion:
There is an association between mobility performance and concern about falling in older adults. Mobility performance metrics can serve as predictors to identify older adults with high concern about falling, potentially providing digital biomarkers for clinicians to remotely track older adults’ change of concern about falling via applications of remote patient monitoring.
Keywords: Mobility performance, Concern about falling, Digital biomarker, Wearables, Remote patient monitoring
Introduction
Concern regarding falling is highly prevalent in community-dwelling older adults, with the prevalence rate estimated between 20.8 to 85% [1–3]. When someone has significant concern about falling, it can lead to a decline in physical and mental performance [1], avoidance of physical activity and social participation [4], increased depression and psychological distress [5], increased physical frailty [4], increased fall risk [4, 6], and reduced quality of life [7]. These adverse consequences may lead to more functional impairment, and ultimately loss of independence [1].
By assessing for concern about falling in older adults, it can facilitate the recommendation of early intervention strategies [8]. Concern about falling can be evaluated by Falls Efficacy Scale-International (FES-I) or Activities-specific Balance and Confidence scale (ABC) [1, 9]. These scales rely on self-report data and therefore are subjective [10, 11]. However, there may be more objective correlates of concern about falling. Previous studies have demonstrated that concern about falling is associated with motor capacity metrics measured under standardized walking conditions in a laboratory environment [12–14]. Additionally, self-reported or sensor-measured physical activity restriction is observed in older adults who present with high concern about falling [14–16].
Recent advances in the development of wearable devices allow for the remote monitoring of ‘mobility performance’ [17, 18]. Mobility performance refers to walking characteristics under a real-life condition in dynamic home and community environments. Mobility performance has already been identified as a new measure of fall risk [19, 20] and cognitive impairment [21, 22]. Compared with motor capacity, mobility performance can be defined more broadly, including walking quantity (e.g., total steps and bouts per day), daily-life gait (e.g., stride velocity and cadence), and walking quality (e.g., symmetry, intensity, and complexity) [17, 23]. However, the relationship between mobility performance and concern about falling.is unknown at present.
As such, the current study investigated the relations between mobility performance metrics measured by a wearable pendant motion sensor and concern about falling of older adults. We hypothesized that older adults with high concern about falling would have lower walking quantity, poorer daily-life gait, and reduced walking quality than those with lower concern about falling. We also examined which mobility performance metrics would be independent digital biomarkers that could predict high concern about falling in older adults.
Methods
Participants
This study is a secondary analysis of baseline data collected within two larger longitudinal studies. The data were from two registered studies with ClinicalTrials.gov, identifier NCT01880229 and NCT02819011. These studies were conducted among community-dwelling older adults aged 65 years or older. The first study assessed a person’s risk of falling and frailty levels objectively based on physical activity data measured by a wearable sensor. The second study evaluated the long-term effects of bilateral customized ankle-foot orthoses in reducing falling risk in older adults. In the current study, only the baseline measurements (participant characteristics and metrics of motor capacity and mobility performance) were included. Across both studies, participants were recruited from primary, secondary, and tertiary health care settings, community providers, assisted living facilities, retirement homes, and aging service organizations. Participant inclusion criteria included being able to walk a distance of at least 1.8 m (~6 feet) without assistance and the use of walking-assistive devices (such as canes) in daily living allowed. Exclusion criteria included a Mini-Mental State Examination score ⩽ 23, terminal illness, and unwillingness to participate. Eligible subjects signed a written informed consent form, approved by local institutional review boards (University of Arizona and Baylor College of Medicine).
Outcome Measures
Participant Characteristics
Participants were interviewed to collect demographic and health information (age, sex, and body mass index-BMI calculated from height and weight). Participants’ depressive symptoms, concern about falling, and cognitive performance were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) [24], the Falls Efficacy Scale-International (FES-I) [25], and the Mini-Mental State Examination (MMSE) [26], respectively. Participants with high concern about falling were categorized based on the cutoff threshold of FES-I ⩾ 23 [9].
Motor Capacity
Participants’ motor capacity was assessed under single- and dual-task (counting backward by 1, starting from 100) walking conditions under supervision of a trained examiner. Participants walked 4.57 m (15 feet) at a self-selected speed under each condition. Spatial-temporal gait metrics (stride velocity, stride time, stride length, and coefficient of variation (CV) of stride velocity) were measured using shank- and waist-attached wearable sensors (LEGSys™, Biosensics, Boston, US) and gait analysis algorithms which have been described previously in details [27].
Mobility Performance
Triaxial accelerometry signals were collected continuously over 48 hours using a pendant motion sensor (PAMSys™, Biosensics, Boston, US). Participants were encouraged to wear it for all time, except while showering. Unbroken walking bouts were detected using the analysis algorithm developed by Najafi et al. [28, 29]. The algorithm has been validated in both older adults with and without using walking assistive devices (such as a cane) [28]. Metrics of walking quantity were extracted from walking bouts with at least three successive steps [28, 30], including the number of walking bouts and steps per day, and walking time (as percentage of total monitoring time), as well as the mean, standard deviation (SD), and maximum of step counts across walking bouts. These walking quantity metrics can represent the physical activity level [31]. Metrics of daily-life gait were extracted from walking bouts with at least ten steps [29, 32], including the means of stride time, step length, stride velocity, and CV of stride velocity across walking bouts, as well as the SD of stride velocity across walking bouts. The SD of stride velocity can represent between-walk adaptability [30]. Metrics of walking quality were extracted from the same walking bouts with at least ten steps, including the means of harmonic ratios in vertical, frontal, and lateral directions across walking bouts, and the means of root mean square (RMS) of trunk acceleration, velocity, and displacement in the vertical direction across walking bouts. Harmonic ratios are an index of gait symmetry [33], and the RMS of the trunk vertical motion can represent gait intensity [34]. In these mobility performance metrics, walking quantity and daily-life gait metrics are direct outputs of the assessment device (e.g., daily walking time and walking steps, and stride velocity), using validated algorithms described in our previous studies [29, 35], or simple descriptive statistics of these outputs (e.g., the mean, SD, and maximum of steps across walking bouts, and the SD of stride velocity across walking bouts). Differently, walking quality metrics (e.g. harmonic ratio) were calculated from raw accelerometer data using well established signal quantification formulas [33, 34]. Table 1 shows names and definitions of all used mobility performance metrics.
Table 1.
Mobility performance metrics and their definitions
| Mobility performance metrics | Definitions |
|---|---|
| Walking quantity | |
| Daily walking bouts, n | Number of walking bouts over 24h. A walking bout was defined by at least three consecutive steps as suggested by Najafi et al [28]. |
| Daily steps, n | Number of walking steps over 24h |
| Daily walking time, % | Time spent in walking as percentage of 24h |
| Walking bout average, n | Mean of step counts of all walking bouts |
| Walking bout variability, n | Standard deviation of step counts of all walking bouts |
| Longest walking bout, n | Maximum of step counts of all walking bouts |
| Daily-life gait | |
| Stride time, s | Mean of stride time of all walking bouts |
| Stride length, m | Mean of stride lengths of all walking bouts |
| Stride velocity, m/s | Mean of stride velocities of all walking bouts |
| Gait unsteadiness, % | Mean of CVs of stride velocities of all walking bouts |
| Gait variability, m/s | Standard deviation of stride velocities of all walking bouts |
| Walking quality | |
| Vertical gait symmetry | Harmonic ratio of vertical trunk acceleration |
| Frontal gait symmetry | Harmonic ratio of frontal trunk acceleration |
| Lateral gait symmetry | Harmonic ratio of lateral trunk acceleration |
| Trunk acceleration intensity, m/s2 | Root mean square of vertical trunk acceleration |
| Trunk velocity intensity, m/s | Root mean square of vertical trunk velocity |
| Trunk displacement intensity, cm | Root mean square of vertical trunk displacement |
Note: CV = coefficients of variation.
Statistical Analyses
All statistical analyses were performed using IBM SPSS Statistics version 24 (IBM, Armonk, NY, USA). For all statistical analyses, significance was accepted at p < 0.05. Participant characteristics across levels of concern about falling (binary: high and low) were compared using one-way analysis of variance (ANOVA) for continuous variables that are normally distributed, or using Mann-Whitney U test for continuous variables that are not normally distributed. Normality was checked using the Shapiro-Wilk test (p > 0.05). Additionally, χ2 test was used to analyze between-group differences for categorical variables.
To more robustly evaluate the influence of concern about falling on mobility performance, propensity score matching was used to generate a matched case-control subsample, reducing confounding effects from participant characteristics except for the grouping factor (FES-I scores) [36, 37]. Participant characteristics that showed significant differences across levels of concern about falling in the total sample before the matching, were selected as covariates for the propensity matching. The match tolerance was set to 0.2. The matching procedure ran a logistic regression on the group indicator (levels of concern about falling), and then used the resulting propensity variable to select controls for the cases without replacement.
Then, based on the post-matched subsample, ANOVA was used to analyze between-group differences for motor capacity and mobility performance metrics. For mobility performance metrics, additional analysis of covariance (ANCOVA) was used to analyze between-group differences for mobility performance metrics again, with adjustment of the motor capacity metrics that showed significant between-group differences in the last step of the ANOVA. Moreover, the effect size (Cohen’s d) was calculated for each metric of motor capacity and mobility performance.
We used hierarchical multiple logistic regression models to identify older participants with high concern about falling. Only the motor capacity and mobility performance metrics that showed significant between-group differences via ANCOVA analyses were considered as predictors. The logistic model includes three blocks: Motor capacity metrics were inputted into the first block, as the control of confounding factors. Then, mobility performance metrics were inputted sequentially, where walking quantity metrics were inputted into the second block as a benchmark, and daily-life gait and walking quality metrics were inputted into the third block. The absence of multicollinearity between the predictors was determined by examining the variance inflation factor (VIF). When two or more correlated predictors had VIFs higher than 10, the one with the lowest effect size would be removed until all predictors’ VIFs were below 10. For each block of this multiple logistic regression model, feature selection of backward stepwise LR method was used. The model performance (Nagelkerke R2, accuracy, change in χ2 and significance (p-value), area under the curve (AUC) from receiver operating characteristics analysis (ROC)), and each predictor’s coefficient (B) and significance (p-value) were reported.
Results
A total of 155 older adults satisfied inclusion and exclusion criteria and were included in this study. The total sample was stratified based on FES-I score into two groups with low concern about falling (N = 63) and high concern about falling (N = 92). There were significant group (low vs. high concern about falling) differences observed in age, BMI, CES-D, MMSE, and FES-I scores. After propensity score matching with age, BMI, CES-D, and MMSE scores, 122 participants (63 participants with low concern about falling and 59 participants with high concern about falling) were retained for the subsequent analyses. Significant differences were no longer observed in BMI, CES-D, and MMSE scores after propensity score matching. Table 2 reports participant characteristics before and after propensity score matching.
Table 2.
Participant characteristics before and after propensity score matching.
| Non-propensity score-matched groups | Low concern about falling | High concern about falling | p-value |
|---|---|---|---|
| Number | 63 | 92 | |
| Age, years | 76.3±7.2 | 79.6±8.8 | 0.036 |
| No (%) of female | 46 (73) | 75 (82) | 0.209 |
| Body mass index (BMI) | 26.9±5.4 | 29.0±6.4 | 0.043 |
| CES-D | 5.9±5.7 | 10.0±7.4 | 0.001 |
| MMSE | 29.1±1.3 | 28.5±1.6 | 0.042 |
| FES-I | 19.1±1.8 | 34.9±10.4 | <0.001 |
| Propensity score-matched groups | |||
| Number | 63 | 59 | |
| Age, years | 76.3±7.2 | 79.3±9.1 | 0.091 |
| No (%) of female | 46 (73) | 47 (80) | 0.389 |
| Body mass index (BMI) | 26.9±5.4 | 28.3±6.3 | 0.292 |
| CES-D | 5.9±5.7 | 7.5±6.4 | 0.141 |
| MMSE | 29.1±1.3 | 28.9±1.3 | 0.310 |
| FES-I | 19.1±1.8 | 32.0±7.7 | <0.001 |
Note: CES-D = Center for Epidemiologic Studies Depression Scale; MMSE = Mini-Mental State Examination; FES-I = Fall Efficacy Scale-International.
Table 3 shows differences between groups (high vs. low concern about falling) for motor capacity. Significant differences were observed for stride velocity and stride length under single- and dual-task walking conditions.
Table 3.
Motor capacity in participants across levels of concern about falling.
| Low concern about falling (N = 63) | High concern about falling (N = 59) | p-value | Effect size | |
|---|---|---|---|---|
| Single-task walking | ||||
| Stride velocity, m/s | 1.11±0.19 | 1.00±0.29 | 0.024 | 0.45 |
| CV of stride velocity, % | 5.3±3.4 | 5.9±3.6 | 0.173 | 0.17 |
| Stride time, s | 1.14±0.13 | 1.19±0.18 | 0.064 | 0.32 |
| Stride length, m | 1.22±0.15 | 1.14±0.23 | 0.029 | 0.41 |
| Dual-task walking | ||||
| Stride velocity, m/s | 0.99±0.23 | 0.89±0.29 | 0.049 | 0.38 |
| CV of stride velocity, % | 7.0±3.3 | 7.9±3.8 | 0.225 | 0.25 |
| Stride time, s | 1.30±0.28 | 1.33±0.26 | 0.352 | 0.11 |
| Stride length, m | 1.22±0.16 | 1.12±0.24 | 0.009 | 0.49 |
Note: CV = coefficients of variation. p-value is calculated from ANOVA; Effect size is calculated using Cohen’s d.
Table 4 shows differences between groups (high vs. low concern about falling) for mobility performance metrics. Significant between-group differences were observed for all metrics in walking quantity, for stride length, stride velocity, and gait variability in daily-life gait, and for vertical and frontal gait symmetry, intensity of trunk acceleration, velocity, and displacement in walking quality. After adjustment with motor capacity metrics (stride velocity and stride length under both single- and dual-task conditions), between-group differences remained statistically significant for all metrics in walking quantity, for stride velocity and gait variability in daily-life gait, and for frontal gait symmetry and intensity of trunk acceleration and velocity in walking quality.
Table 4.
Mobility performance of participants across levels of concern about falling.
| Low concern about falling (N = 63) | High concern about falling (N = 59) | p-value1 | p-value2 | Effect size | |
|---|---|---|---|---|---|
| Walking quantity | |||||
| Daily walking bouts, n | 183.8±78.1 | 125.9±68.9 | <0.001 | <0.001 | 0.79 |
| Daily steps, n | 4745.0±2432.4 | 2726.7±1639.0 | <0.001 | <0.001 | 0.97 |
| Daily walking time, % | 7.0±3.9 | 3.9±2.6 | <0.001 | <0.001 | 0.94 |
| Walking bout average, n | 32.3±11.7 | 26.3±8.0 | 0.001 | 0.004 | 0.97 |
| Walking bout variability, n | 73.7±68.6 | 36.2±37.9 | 0.002 | 0.001 | 0.68 |
| Longest walking bout, n | 948.9±1050.7 | 449.8±810.1 | 0.004 | 0.013 | 0.53 |
| Daily-life gait | |||||
| Stride time, s | 1.64±0.15 | 1.66±0.15 | 0.520 | 0.583 | 0.13 |
| Stride length, m | 1.36±0.16 | 1.27±0.19 | 0.009 | 0.123 | 0.51 |
| Stride velocity, m/s | 0.95±0.12 | 0.87±0.13 | 0.001 | 0.023 | 0.64 |
| Gait unsteadiness, % | 40.8±5.8 | 41.7±5.3 | 0.384 | 0.944 | 0.16 |
| Gait variability, m/s | 0.23±0.05 | 0.19±0.05 | <0.001 | 0.005 | 0.80 |
| Walking quality | |||||
| Vertical gait symmetry | 1.65±0.25 | 1.55±0.24 | 0.032 | 0.100 | 0.41 |
| Frontal gait symmetry | 1.53±0.25 | 1.42±0.20 | 0.008 | 0.041 | 0.49 |
| Lateral gait symmetry | 1.73±0.44 | 1.69±0.31 | 0.820 | 0.309 | 0.11 |
| Trunk acceleration intensity, m/s2 | 1.03±0.17 | 0.92±0.16 | 0.002 | 0.011 | 0.67 |
| Trunk velocity intensity, m/s | 0.08±0.01 | 0.07±0.01 | 0.002 | 0.017 | 1.00 |
| Trunk displacement intensity, cm | 1.18±0.23 | 1.08±0.20 | 0.010 | 0.057 | 0.46 |
Note: p-value1 is calculated from ANOVA analysis without adjustment; p-value2 is calculated from ANCOVA analysis with adjustment of the selected motor capacity metrics (stride velocity and stride length under both single- and dual-task conditions); Effect size is calculated using Cohen’s d.
Table 5 shows predictors and model performance of the hierarchical logistic regression analysis. When only entering Block 1 (motor capacity metrics), the metric of stride length under dual-task walking condition was selected as an independent predictor (p = 0.002), and the model accounted for 9.0% of the explained variability (R2 = 0.090) with the accuracy of 62.7%. When further entering Block 2 (walking quantity metrics), the metric of daily steps was added into the model as an independent predictor (p < 0.001), but at this time the metric of stride length under dual-task walking condition was no longer the significant predictor (p = 0.318). The model with Blocks 1 and 2 accounted for 29.7% of the explained variability (R2 = 0.297) with the accuracy of 66.9%. Compared with the model with only Block 1, the model with Blocks 1 and 2 identified the group with high concern about falling significantly better (χ2 = 21.556, p < 0.001). Finally, after entering Block 3 (daily-life gait and walking quality metrics), the metric of frontal gait symmetry was added into the model as an independent predictor (p = 0.042), and the metric of daily steps was still an independent predictor (p < 0.001), while the metric of stride length under dual-task walking condition was not the independent predictor (p = 0.501). Compared with the model with Blocks 1 and 2, the model with all three blocks identified the group with high concern about falling significantly better (χ2 = 4.378, p = 0.036). Figure 1 shows the ROC curves of the logistic regression model. By sequentially adding the blocks into the model, the AUC increased from 0.644, to 0.747 and to 0.772.
Table 5.
Logistic regression results to identify participants with high concern about falling.
| Predictors | Results | ||||
|---|---|---|---|---|---|
| Name | B (p-value) | R2 | Accuracy (%) | Δχ2 (p-value) | |
| Block 1: motor capacity | DTW stride length | −0.698 (0.002) | 0.090 | 62.7 | 29.778 (0.004) |
| Blocks 1+2: motor capacity + walking quantity | DTW stride length | −1.106 (0.318) | 0.297 | 66.9 | 21.556 (<0.001) |
| Daily steps | −0.001 (<0.001) | ||||
| Blocks 1+2+3: motor capacity + walking quantity + daily-life gait + walking quality | DTW stride length | −0.762 (0.501) | 0.335 | 72.0 | 4.378 (0.036) |
| Daily steps | −0.001 (<0.001) | ||||
| Frontal gait symmetry | −2.003 (0.042) | ||||
Note: DTW = dual-task walking. B: coefficients of determinants. R2 represents percentage of the explained variability. Accuracy represents classification ability of the model to identify those with the high concern about falling. Δχ2 represent the change of the model performance as a new block is added into the model, where the Δχ2 for the model with only Block 1 (the first data row) represents the improvement between the model with Block 1 and the model without only constant.
Figure 1.
ROC curves for the hierarchical logistic regression model with the three blocks (motor capacity, walking quantity, and habitual gait and walking quality). The AUC values are 0.644, 0.747, and 0.772 for different levels of this model.
Discussion
Our results indicate that older adults who present with high concern about falling demonstrate: lower walking quantity (daily walking bouts, daily steps, daily walking time), poorer daily-life gait (stride velocity, gait variability), and poorer walking quality (frontal gait symmetry, trunk acceleration and velocity intensity), after controlling for participant characteristics (age, BMI, CES-D, and MMSE) and motor capacity (stride velocity and stride length under both single- and dual-task walking conditions). We also found that selected mobility performance metrics (daily steps and frontal gait symmetry) were significant predictors of the group with high concern about falling after controlling for motor capacity metrics (stride length of dual-task walking), indicating that these mobility performance metrics may be useful to triage objectively older adults with high concern about falling.
Notably, our participants with high concern about falling were significantly older and had increased depression, more cognitive impairment, and greater BMI relative to those with fewer falling concerns (Table 2). These findings are congruent with the epidemiological studies [7, 38–40]. Additionally, significant reductions were found in motor capacity metrics (stride velocity, stride length under single- and dual-task walking conditions) in older participants with high concern about falling, compared with those with low concern about falling (Table 3). This finding indicates a potential cognitive influence on walking capacity (e.g., fear of falling, anxiety about falling), which is in line with previous studies [12–14].
More importantly, this is the first study to demonstrate the influence of concern about falling on mobility performance, including not only walking quantity but also habitual gait and walking quality. After adjustment for motor capacity (stride velocity and stride length in single- and dual-task standardized walking), some mobility performance metrics (such as daily walking bouts, steps, and walking time, walking bout average, walking bout variability, longest walking bout, stride velocity, gait variability, frontal gait symmetry, and trunk acceleration and velocity intensity) still showed significant differences across groups with different levels of concern about falling (Table 4). The results reflect the adverse influence of concern about falling on daily walking behavior irrespective of walking capacity. This finding provides a novel insight into influence of concern about falling to daily walking activity, extending from traditional physical activity restriction [14–16]. Additionally, these impaired mobility performance metrics were found to be associated with retrospective and prospective falls in previous studies [19, 20, 34], and may play a role as mediators between concern about falling and prospective falls.
Few studies investigated sensor-derived physical activity (walking quantity) across levels of concern about falling. Paraschiv-Ionescu et al. reported that there were no significant differences across levels of concern about falling for time spent for walking, steps of the longest walking bout (max steps of all walking bouts), and average cadence (an inverse form of stride time) [41]. These findings do not match consensus obtained from the known questionnaire-based studies [2, 42], and it may because of underpowered statistical comparison on relatively small sample size (N = 40). In contrast, we observed significant between-group differences in walking quantity (see Table 4) based on our post-matched subsample with more than 100 participants. The reduction in walking quantity in participants with high concern about falling may indicate fear-related avoidance of healthy lifestyles, such as outdoor walking, leisure-time physical activity, and access to recreational or community facilities.
The final prediction model for high concern about falling comprised only two mobility performance metrics (daily steps and frontal gait symmetry) as significant predictors with one motor capacity metric (stride length under dual-task walking condition) as a covariate (Table 5). None of the daily-life gait metrics were included in the final model. It may because there is a strong correlation between motor capacity (clinical gait) and daily-life gait. The sign symbols of the two independent predictors’ coefficients in the final model may indicate that lower daily steps and frontal gait symmetry in mobility performance are associated with an increased likelihood of having a high concern about falling. The results suggest that the selected mobility performance metrics could serve as objective indicators from which to triage the individual with potentially high concern about falling for further in-clinic evaluation or tele-medicine consultation. Compared with direct investigation of concern about falling using the questionnaire (e.g. FES-I), the sensor-based mobility performance metrics may provide a chance to track change of concern about falling, even before the individual has an insight into the subtle change.
One limitation of our study on the technical side is that the mobility performance metrics were derived from 48-hour continuous accelerometry data recording, which may not cover the day-to-day variability of physical activity patterns among community-dwelling older adults. There is still controversy about the minimum number of days required for assessing mobility performance. The periods of previous studies varied from 2 days to 14 days [23, 41]. In the future, we need to investigate whether the day-to-day variability of mobility performance will influence the identification of levels of concern about falling for our applications.
Several limitations were also generated from our sample. Besides the limited sample size, the generalizability of our results to the general older population may be limited by the fact that only cognitive-intact older people were included in this study. A social desirability bias (a psychological influence that the participant knows a sensor monitors him/her) may cause the older participants to have higher physical activity level than a normal condition. Additionally, we must acknowledge that propensity score matching used in the current analyses could not entirely exclude the other potential confounding factors which we did not measure. Future studies are needed to verify whether the association between mobility performance and concern about falling still exists in a larger sample across various patient populations (e.g., cognitive-impaired, or those with different comorbidities) within improved study designs (e.g., prospective observational studies, or longitudinal studies). Additionally, practicability, acceptance, and adherence to wearing the sensor would also have to be considered and investigated.
In conclusion, there is a significant association between mobility performance and concern about falling. The selected mobility performance metrics can serve as predictors to identify older adults with high concern about falling, potentially providing digital biomarkers for clinicians to remotely track older adults’ change in levels of concern about falling in applications of remote patient monitoring.
Acknowledgement:
The authors would like to acknowledge Ms. Maria Noun, Ms. Sogol Golafshan, and Ms. Sanam Sharafkhaneh who contributed to data management. The authors also would like to give special thanks to Ms. Qianzi Zhang for her support on statistical analyses.
This work was supported by the National Institutes of Health/National Institute on Agings (award numbers SB1AG032748 and R42AG060853). M.P. is supported through funding from The Menninger Clinic and The Menninger Clinic Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.
Footnotes
Conflict of interest statement: AV is with BioSensics LLC, manufacturing of PAMsys used in this study. He is also serving as the Principal Investigator of a NIH SBIR grant, which partially sponsored this study. He has however only contributed in the technical aspect of this study and didn’t involve in patient recruitment, data analysis or interpretation of clinical data. None of other coauthors claimed conflict of interest relevant to the scope of this study.
Statement of Ethics: The data were from two registered studies with ClinicalTrials.gov, identifier NCT01880229 and NCT02819011. Eligible subjects signed a written informed consent form, approved by local institutional review boards (University of Arizona and Baylor College of Medicine).
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