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Background:
Brief Report

Smoothness Evaluation Indices during Sit-to-Stand-to-Sit Motions in Healthy Older Females and after Hip Fracture Using an Accelerometer: A Pilot Study

by
Takeshi Shimamura
1,2,*,
Hitoshi Ishikawa
2,
Hiromi Fujii
2 and
Hiroshi Katoh
2
1
Department of Rehabilitation, Kumamoto Health Science University, 325 Izumi-machi, Kita-ku, Kumamoto 861-5598, Japan
2
Graduate School of Health Sciences, Yamagata Prefectural University of Health Sciences, 260 Kamiyanagi, Yamagata 990-2212, Japan
*
Author to whom correspondence should be addressed.
Geriatrics 2023, 8(5), 98; https://doi.org/10.3390/geriatrics8050098
Submission received: 4 July 2023 / Revised: 27 August 2023 / Accepted: 29 September 2023 / Published: 1 October 2023
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Abstract

:
Background: Studies that quantify the quality of sit-to-stand-to-sit (STS) motions, particularly in terms of smoothness, are limited. Thus, this study aimed to investigate the possibility and usefulness of quality evaluation during STS motions. Methods: This cross-sectional study enrolled 36 females aged >60 years, including 18 females each in the healthy and hip fracture groups. Measurements were performed at two different speeds: five STS as fast as possible (STSF) and two seconds for each motion (STS2s). Indices of smoothness, including harmonic ratio (HR) and power spectrum entropy (PSE), were calculated and compared from the measured data in each of the three axial directions. Results: HR in the vertical direction was significantly higher in the healthy group (STSF: 3.65 ± 1.74, STS2s: 3.42 ± 1.54) than in the hip fracture group (STSF: 2.67 ± 1.01, STS2s: 2.58 ± 0.83) for STSF and STS2s. Furthermore, PSE for all directions and triaxial composites were significantly lower for STS2s (the healthy group (mediolateral (ML): 7.63 ± 0.31, vertical (VT): 7.46 ± 0.22, anterior–posterior (AP): 7.47 ± 0.15, triaxial: 7.45 ± 0.25), the hip fracture group (ML: 7.82 ± 0.16, VT: 7.63 ± 0.16, AP: 7.61 ± 0.17, triaxial: 7.66 ± 0.17)). Conclusions: This study suggests the usefulness of HR and PSE as quality evaluations for STS motions.

1. Introduction

Motion analysis is essential for rehabilitation to improve the basic actions of sit-to-stand-to-sit (STS) and gait motions. Several motion analyses of gait motion have been explored in the field of rehabilitation [1]. Gait analysis can be performed using various methods, including quantitative measures such as speed, stride length, and gait ratio [2] as well as the evaluation of motion quality by analyzing acceleration. In recent years, studies using accelerometers have been increasing in clinical settings as it is a simple evaluation tool with few limitations on measurement [3]. In particular, harmonic ratio (HR) and power spectrum entropy (PSE) have been used as indices of motion quality in previous studies focusing on smoothness during gait. HR evaluates the regularity, harmony, and symmetry of behavior [4,5,6], and stable behavior has been reported to be associated with higher values. PSE evaluates smoothness based on the probability of superimposing higher frequencies [7,8,9], and it has been reported that good behavior is associated with lower values. Particularly in older adults, the PSE of maximum speed gait is less correlated with physical functions such as grip strength; however, a distinction has been made between those with and without a history of falls [7]. These smoothness indices are effective in providing information that is not provided by quantitative indices [7,10], thus indicating their usefulness in clinical settings.
Conversely, STS is a pre- and post-gait motion essential for smooth activities of daily living (ADL). The 30 s standing test using frequency and the 5 sit-to-stand test (5STS) measuring motion time are widely used to assess STS ability, and the association between motion time and worsening ADL in older adults has been reported [11]. Additionally, STS ability has been associated with maximum gait speed [12], frailty and gait speed, and ADL and quality of life disability in females >60 years old [13]. Furthermore, Bagalà et al. [14] used accelerometers to measure variations in acceleration during lie-to-sit-to-stand-to-walk movements and reported their usefulness in assessing the quality of performance. Zablotny et al. [15] reported that observational analysis of sit-to-stand motion in older adults after hip fracture can be supported with quantitative data to evaluate intervention. Other researchers used accelerometers to evaluate the time and speed of mobility [16], whereas some examined the amount of physical activity after fracture [17]. Although many studies have evaluated and examined movement qualities such as smoothness [5,7,14,18], to the best of our knowledge, none of the studies have used smoothness indices in STS motion. Therefore, this study aimed to investigate the differences in the smoothness of motion during STS in healthy older adults and postoperative patients with hip fractures using quality indices. In this study, it was hypothesized that the healthy group had a higher quality of movement, higher HR, and lower PSE than the hip fracture group.

2. Materials and Methods

2.1. Study Design

The present study is a cross-sectional study.

2.2. Participants

The participants included 18 healthy women aged ≥60 years without psychiatric, neurological, or musculoskeletal diseases and without any history of diseases affecting movement (healthy group) and 18 women with hip fractures with Mini-Mental State Exam scores of ≥24 without any history of previous dementia who were able to perform the movement and consented to participate in the study (hip fracture group) (Figure 1). This study was approved by the Ethics Committee of Kumamoto Kenhoku Hospital (2020-012). Data collection was conducted 22 months after obtaining ethics approval.

2.3. Ethical Procedures

All participants were informed about the purpose of the study as per the Declaration of Helsinki, and their informed consent was obtained before starting the study.

2.4. Measurements

The 9-axis wireless motion sensor SS-MS-HMA16G15 (Motion Sensor) (Sports Sensing Inc., Fukuoka, Japan) was used for measurement. It was 53, 38, and 11 mm in length, width, and thickness, respectively. The 9 axes comprised 3-axis acceleration (16 G), 3-axis angular velocity (1500 dps), and 3-axis geomagnetic sensor (10 gauss). Acceleration was noted in 3 axes: mediolateral (ML), vertical (VT), and anterior–posterior (AP) directions. The motion sensor was attached to the participant’s third lumbar spinous process (L3) using an elastic band, following the method of Moe Nilssen et al. (Figure 2) [19]. The sampling frequency was set at 100 Hz. The task motion included five STS repetitions at two different speeds and one maximum speed gait. The STS measurements began with the participants sitting in a chair without a backrest or armrests, with their arms crossed on their chests [20,21,22]. Seat height was adjusted using adjustable chairs, hip and knee joints were set at approximately 90° [22], and the distance between the right and left feet was the width of the superior anterior iliac spine [20]. The first task motion consisted of five STS fast (STSF) repetitions at maximal speed, whereas the second was performed using a metronome to control sit-to-stand and stand-to-sit for 2 s (STS2s) each [23] for five repetitions. Measurements were taken after practice with a 1 min rest period. Gait motion was measured for a 10 m steady-state gait with 3 m aided paths in front and behind. The instructions for the maximum speed gait were “please gait as fast as you can” [12]. Rest intervals of ≥1 min were provided to verbally confirm that there was no fatigue before proceeding to the next measurement. Measurements of the hip fracture group were estimated for 7 weeks postoperatively and were performed within 3 days of the measurement being possible.

2.5. Data Processing

Analysis parameters included 5STS, HR, and PSE, and the acceleration data were analyzed after low-pass filtering using a Butterworth filter of 20 Hz [5]. STS motion analysis is affected by the tilt of the motion sensor during motion in the 3-axis acceleration data and by gravity due to the difference between relative and absolute coordinates. Thus, the VT and AP directions, in which the effects of gravity are greater, were corrected [23]. STS detection was determined at the point at which the angular velocity of the frontal axis during motion switched from negative to positive after two repetitions of acceleration and deceleration (Figure 3). To calculate the indices, the start of motion was defined as the point at which the angular velocity of the frontal axis during motion exceeded three times the standard deviation of the mean angular velocity in the sitting position. In the gait motion, the walking cycle was extracted by referring to the peak value before the change in AP acceleration from positive to negative with heel contact occurred (Figure 3) [24]. Triaxial composite direction analysis was conducted using the square root of the sum of the squared accelerations in the three directions.

2.5.1. Five Sit-to-Stand Test

5STS was calculated from STSF and was defined as the time from the onset of motion to the end of the fifth standing.

2.5.2. Harmonic Ratio

HR in the STS motion was analyzed as one cycle for each of the sit-to-stand and stand-to-sit motions owing to the fact that the directions of these motions are different. The discrete Fourier transform and the even/odd of the first twenty harmonic coefficients [5,6,25], two sit-to-stand, and two stand-to-sit were used to calculate HR (Figure 4). Representative values include the average of the two values calculated 2–3 and 3–4 times.

2.5.3. Power Spectrum Entropy

Fast Fourier transform with Hanning’s window function and normalized time series spectrum after excluding the first component were used to calculate PSE (Figure 4) [7,8,9].

2.6. Statistical Analyses

The normality of the data was assessed using the Shapiro–Wilk test, and equal variance was assessed using the Levene test. Moreover, the two-sample t-test and Mann–Whitney U test were used to compare the participants in the healthy and hip fracture groups. Effect size (ES) was calculated in terms of Cohen’s d, where d = 0.2, 0.5, and 0.8 representing small, medium, and large effect sizes, respectively [26]. Notably, p-values of <0.05 were considered statistically significant. R4.2.1 (CRAN, freeware) for Windows was used to perform all statistical analyses. During the comparison of 5STS and maximum gait speed (MGS), analysis of covariance adjusted for age and weight was used to evaluate the results. Further, the sample size was calculated using G*Power 3.1.9.7 (Heinrich Heine University, Dusseldorf). The significance level was set at α = 0.05, power was set at 1 − β = 0.8, ES (d) of 0.988 was assumed in the preliminary study, and the minimum number of participants in the group was recommended to be 18.

3. Results

3.1. Characterization of the Sample and Comparison of the Two Study Subgroups

Age was significantly lower in the healthy group (63.1 ± 3.2 years) than in the hip fracture group (76.2 ± 7.8 years), but weight and body mass index (BMI) were significantly higher in the healthy group participants (weight: 52.6 ± 9.0 kg, BMI: 22.1 ± 3.1 kg/m2) than in the hip fracture group participants (weight: 45.3 ± 5.7 kg, BMI: 20.0 ± 2.4 kg/m2) (Table 1).

3.2. Five Sit-to-Stand Test Compared with Maximum Gait Speed

The time required for 5STS was significantly lower in the healthy group (8.75 ± 1.05 s) than in the hip fracture group (12.17 ± 3.20 s) (p < 0.001, ES = 1.43). The MGS was significantly higher in the healthy group (1.93 ± 0.21 s) than in the hip fracture group (1.09 ± 0.27 s) (p < 0.001, ES = 3.51). Notably, the analysis of covariance with age and BMI as covariates revealed that MGS was significantly higher in the healthy group than in the hip fracture group (p = 0.012) (Table 2).

3.3. Harmonic Ratio Comparison

The HR for STSF motion in the VT direction was significantly higher in the healthy group (3.65 ± 1.74) than in the hip fracture group (2.67 ± 1.01) (p = 0.047, ES = 0.69); moreover, HR for STS2s motion in the VT direction was significantly higher in the healthy group (3.42 ± 1.54) than in the hip fracture group (2.58 ± 0.83) (p = 0.049, ES = 0.69) (Table 3).

3.4. Power Spectrum Entropy Comparison

STSF analyzed 512 intermediate movements (5.12 s), whereas STS2s analyzed 2048 intermediate movements (20.48 s). The PSE for STSF motion was significantly higher in the healthy group (AP: 5.78 ± 0.16, triaxial: 5.85 ± 0.21) than in the hip fracture group (AP: 5.61 ± 0.16, triaxial: 5.68 ± 0.18) in the AP (p = 0.002, ES = 1.10) and triaxial composite (p = 0.015, ES = 0.85) directions. Meanwhile, the PSE for STS2s motion was significantly lower in the healthy group (ML: 7.63 ± 0.31, VT: 7.46 ± 0.22, AP: 7.47 ± 0.15, triaxial: 7.45 ± 0.25) than in the hip fracture group (ML: 7.82 ± 0.16, VT: 7.63 ± 0.16, AP: 7.61 ± 0.17, triaxial: 7.66 ± 0.17) in the ML (p = 0.035, ES = 0.74), VT (p = 0.010, ES = 0.88), AP (p = 0.014, ES = 0.86), and triaxial composite (p = 0.003, ES = 0.97) directions (Table 4).

4. Discussion

In the present study, the average time for 5STS was 8.75 s in the healthy group. In previous studies on healthy participants by age, it has been reported that the average time taken for 5STS was 7.8, 9.3, and 10.8 s for participants in their 60s, 70s, and 80s [27], and the required time tended to be longer in older age groups. The values for healthy participants in the present study are considered to be reasonable when compared with the values reported in other studies [28,29]. By contrast, in the hip fracture group, the 5STS of three participants in their 60s, nine in their 70s, and five in their 80s were 9.7 ± 0.9, 12.0 ± 2.9, and 12.5 ± 2.8 s, respectively. The average 5STS of the hip fracture group was 12.17 s, suggesting that the participants of this group took a significantly longer time than those of the healthy group of the same age in previous studies.
Further, the mean MGS was 1.93 m/s and 1.09 m/s in the healthy and hip fracture groups, respectively, indicating lower speed in the hip fracture group than in the healthy group. Regarding previous studies, the MGS of healthy females aged ≥60 years was 1.9 m/s [12] and that of females aged ≥70 years was 1.21 m/s [30]. These results suggest that patients in the hip trauma group are more likely to have decreased STS and gait performance than healthy older adults in the same age group.
HR has been analyzed for two cycles with one step as one cycle in previous studies on gait motion, and the more the value increases, the more harmonic and smooth the motion [4,6]. This study attempted to analyze one sit-to-stand and one stand-to-sit cycle as one cycle and revealed a significantly higher HR during STS in the healthy group than in the hip fracture group in both STSF and STS2s in the VT direction. Thus, HR demonstrated the potential of evaluating the smoothness of STS. According to Pai et al., the increase in momentum during controlled sit-to-stand motion was smaller in the AP direction than in the VT direction due to changes in velocity, suggesting a difference in the strategy [31]. Therefore, the STS is primarily in the direction of gravity (antigravity), suggesting its usefulness in evaluating the main direction of motion. Additionally, the ESs for both STSF and STS2s were medium in the VT and triaxial composite directions, suggesting the usefulness of the triaxial evaluation.
PSE was reported to be smooth at low values and otherwise at high values [8]. The PSE for STSF was higher in the healthy group, indicating decreased motion smoothness in the healthy group. A previous study on gait motion reported PSE as independent of motion speed [7]; however, the analysis characteristics revealed the necessity to have an equal number of data for the comparison. Additionally, they reported that PSE is applied to movements for which time series spectra are obtained, but cyclic movements are desired [8]. Each cycle takes >1.5 s during STS, which is longer than gait, corresponding to 1–3 cycles in the 5.12 s analysis. Therefore, the different motion speeds in STSF may affect the value because of the different number of cycles included in the analysis.
By contrast, the STS2s evaluation aligned motion speed with the number of analysis cycles, and PSE was significantly lower in the healthy group for all directions and triaxial composites, suggesting the usefulness of quality evaluation. Furthermore, the ES was medium in the ML direction and large in the VT, AP, and triaxial composite directions, indicating the potential of the evaluation of ES in the triaxial composite direction by specifying the motion. Thus, PSE for STSF is difficult to compare between groups with remarkably different maximum speeds, and comparisons may be possible if STS2s motions were feasible. Hence, the evaluation of PSE for STS between groups requires the specification of the motion, and smoothness can be evaluated by aligning the amount of analysis data. The contents of the motion specifications and analysis methods for future tasks should be considered for STS2s evaluation for persons unable to perform it due to the large load.

Limitations

This study has some limitations. First, it was not possible to exclude the effects of age and BMI in healthy older women (≥60 years) and in patients after hip surgery. Future comparisons with healthy groups including age and BMI are warranted. Second, it was very difficult to generalize the results of our study because the characteristics of men were not identified in the study. Third, because a 20 Hz low-pass filter was used during data processing, the effect of higher frequencies remains unclear. Fourth, the measurements were performed in the period of preparation for hospital discharge, which may have affected the results owing to anxiety about movement among the participants. Further studies to examine the ease of assessment and the amount of exercise load as well as the abovementioned factors are needed to utilize the results of our study for effective rehabilitation.

5. Conclusions

The present study examined the possibility of evaluating quality indicators for sit-to-stand-to-sit. Harmonic ratio and power spectrum entropy for sit-to-stand-to-sit were rarely reported in previous studies and have a higher motion load than gait, but they provide a new perspective on evaluation when determined at the appropriate time. The evaluation of the smoothness of motion at first is useful by evaluating harmonic ratio for sit-to-stand-to-sit fast, followed by power spectrum entropy and harmonic ratio for sit-to-stand and stand-to-sit for 2 s. The addition of these quality evaluations to the quantitative evaluation would assist in more appropriate motion evaluation and intervention consideration.

Author Contributions

T.S., H.I., H.F. and H.K. contributed to the conception and design of this study. T.S. wrote the manuscript. H.K. supervised the data analysis and advised on the data analysis and contributed to the interpretation of the results. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was approved by the Ethics Committee of Kumamoto Kenhoku Hospital (2020-012). All participants were informed about the purpose of the study in accordance with the Declaration of Helsinki, and their informed consent was obtained before starting the study.

Informed Consent Statement

All participants provided informed consent before their participation.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Kumamoto Kenhoku Hospital. We also thank the participants of this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Treacy, D.; Hassett, L.; Schurr, K.; Fairhall, N.J.; Cameron, I.D.; Sherrington, C. Mobility training for increasing mobility and functioning in older people with frailty. Cochrane Database Syst. Rev. 2022, 2022, CD010494. [Google Scholar] [CrossRef]
  2. Dapp, U.; Vinyard, D.; Golgert, S.; Krumpoch, S.; Freiberger, E. Reference values of gait characteristics in community-dwelling older persons with different physical functional levels. BMC Geriatr. 2022, 22, 713. [Google Scholar] [CrossRef] [PubMed]
  3. Kobsar, D.; Charlton, J.M.; Tse, C.T.F.; Esculier, J.-F.; Graffos, A.; Krowchuk, N.M.; Thatcher, D.; Hunt, M.A. Validity and reliability of wearable inertial sensors in healthy adult walking: A systematic review and meta-analysis. J. Neuroeng. Rehabil. 2020, 17, 62. [Google Scholar] [CrossRef] [PubMed]
  4. Doi, T.; Hirata, S.; Ono, R.; Tsutsumimoto, K.; Misu, S.; Ando, H. The harmonic ratio of trunk acceleration predicts falling among older people: Results of a 1-year prospective study. J. Neuroeng. Rehabil. 2013, 10, 7. [Google Scholar] [CrossRef]
  5. Iosa, M.; Bini, F.; Marinozzi, F.; Fusco, A.; Morone, G.; Koch, G.; Cinnera, A.M.; Bonnì, S.; Paolucci, S. Stability and Harmony of Gait in Patients with Subacute Stroke. J. Med. Biol. Eng. 2016, 36, 635–643. [Google Scholar] [CrossRef]
  6. Bellanca, J.; Lowry, K.; VanSwearingen, J.; Brach, J.; Redfern, M. Harmonic ratios: A quantification of step to step symmetry. J. Biomech. 2013, 46, 828–831. [Google Scholar] [CrossRef]
  7. Kojima, M.; Obuchi, S.; Henmi, O.; Ikeda, N. Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series. J. Phys. Ther. Sci. 2008, 20, 243–248. [Google Scholar] [CrossRef]
  8. Kojima, M.; Obuchi, S.; Mizuno, K.; Henmi, O.; Ikeda, N. Power spectrum entropy of acceleration time-series during movement as an indicator of smoothness of movement. J. Physiol. Anthropol. 2008, 27, 193–200. [Google Scholar] [CrossRef]
  9. Kojima, M.; Obuchi, S. Evaluating the usefulness of power spectrum entropy of acceleration time-series in setting the load for high-intensity strength training for the frail elderly. Rigakuryoho Kagaku. 2012, 27, 291–296. (In Japanese) [Google Scholar] [CrossRef]
  10. Brach, J.S.; McGurl, D.; Wert, D.; VanSwearingen, J.M.; Perera, S.; Cham, R.; Studenski, S. Validation of a measure of smoothness of walking. J. Gerontol. A Biol. Sci. Med. Sci. 2011, 66, 136–141. [Google Scholar] [CrossRef]
  11. Wang, D.X.; Yao, J.; Zirek, Y.; Reijnierse, E.M.; Maier, A.B. Muscle mass, strength, and physical performance predicting activities of daily living: A meta-analysis. J. Cachex Sarcopenia Muscle 2019, 11, 3–25. [Google Scholar] [CrossRef] [PubMed]
  12. Alcazar, J.; Kamper, R.S.; Aagaard, P.; Haddock, B.; Prescott, E.; Ara, I.; Suetta, C. Relation between leg extension power and 30-s sit-to-stand muscle power in older adults: Validation and translation to functional performance. Sci. Rep. 2020, 10, 16337. [Google Scholar] [CrossRef] [PubMed]
  13. Baltasar-Fernandez, I.; Alcazar, J.; Mañas, A.; Alegre, L.M.; Alfaro-Acha, A.; Rodriguez-Mañas, L.; Ara, I.; García-García, F.J.; Losa-Reyna, J. Relative sit-to-stand power cut-off points and their association with negatives outcomes in older adults. Sci. Rep. 2021, 11, 19460. [Google Scholar] [CrossRef] [PubMed]
  14. Bagalà, F.; Klenk, J.; Cappello, A.; Chiari, L.; Becker, C.; Lindemann, U. Quantitative Description of the Lie-to-Sit-to-Stand-to-Walk Transfer by a Single Body-Fixed Sensor. IEEE Trans. Neural. Syst. Rehabil. Eng. 2013, 21, 624–633. [Google Scholar] [CrossRef]
  15. Zablotny, C.; Hilton, T.; Riek, L.; Kneiss, J.; Tome, J.; Houck, J. Validity of Visual Assessment of Sit to Stand After Hip Fracture. J. Geriatr. Phys. Ther. 2020, 43, 12–19. [Google Scholar] [CrossRef]
  16. Marques, D.L.; Neiva, H.P.; Pires, I.M.; Marinho, D.A.; Marques, M.C. Accelerometer data from the performance of Sit-to-Stand test by elderly people. Data Brief 2020, 33, 106328. [Google Scholar] [CrossRef]
  17. Kampe, K.; Pfeiffer, K.; Lindemann, U.; Schoene, D.; Taraldsen, K.; Rapp, K.; Becker, C.; Klenk, J. Change of physical activity parameters of hip and pelvic fracture patients during inpatient rehabilitation and after discharge: Analysis of global and in-depth parameters. Eur. Rev. Aging Phys. Act 2021, 18, 9. [Google Scholar] [CrossRef]
  18. Jarchi, D.; Pope, J.; Lee, T.K.M.; Tamjidi, L.; Mirzaei, A.; Sanei, S. A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications. IEEE Rev. Biomed. Eng. 2018, 11, 177–194. [Google Scholar] [CrossRef]
  19. Moe-Nilssen, R. Test-retest reliability of trunk accelerometry during standing and walking. Arch. Phys. Med. Rehabil. 1998, 79, 1377–1385. [Google Scholar] [CrossRef]
  20. Aylar, M.F.; Dionisio, V.C.; Jafarnezhadgero, A. Do the center of mass strategies change with restricted vision during the sit-to-stand task? Clin. Biomech. 2019, 62, 104–112. [Google Scholar] [CrossRef]
  21. Orakifar, N.; Shaterzadeh-Yazdi, M.J.; Salehi, R.; Mehravar, M.; Namnik, N. Muscle Activity Pattern Dysfunction During Sit to Stand and Stand to Sit in the Movement System Impairment Subgroups of Low Back Pain. Arch. Phys. Med. Rehabil. 2019, 100, 851–858. [Google Scholar] [CrossRef] [PubMed]
  22. Orange, S.T.; Metcalfe, J.W.; Liefeith, A.; Jordan, A.R. Validity of various portable devices to measure sit-to-stand velocity and power in older adults. Gait Posture 2020, 76, 409–414. [Google Scholar] [CrossRef] [PubMed]
  23. Shimamura, T.; Katoh, H. Validation of smoothness evaluation during standing and sitting motions using an accelerometer. J. Phys. Ther. Sci. 2021, 33, 246–249. [Google Scholar] [CrossRef] [PubMed]
  24. Zijlstra, W.; Hof, A.L. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 2003, 18, 1–10. [Google Scholar] [CrossRef] [PubMed]
  25. Menz, H.B.; Lord, S.R.; Fitzpatrick, R.C. Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait Posture 2003, 18, 35–46. [Google Scholar] [CrossRef] [PubMed]
  26. Bohannon, R.W.; Bubela, D.J.; Magasi, S.R.; Wang, Y.-C.; Gershon, R.C. Sit-to-stand test: Performance and determinants across the age-span. Isokinet. Exerc. Sci. 2010, 18, 235–240. [Google Scholar] [CrossRef] [PubMed]
  27. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; L. Erlbaum Associates: Hillsdale, NJ, USA, 1988; ISBN 978-0-8058-0283-2. [Google Scholar]
  28. Baltasar-Fernandez, I.; Alcazar, J.; Losa-Reyna, J.; Soto-Paniagua, H.; Alegre, L.M.; Takai, Y.; Ruiz-Cárdenas, J.D.; Signorile, J.F.; Rodriguez-Mañas, L.; García-García, F.J.; et al. Comparison of available equations to estimate sit-to-stand muscle power and their association with gait speed and frailty in older people: Practical applications for the 5-rep sit-to-stand test. Exp. Gerontol. 2021, 156, 111619. [Google Scholar] [CrossRef]
  29. Narazaki, K.; Matsuo, E.; Honda, T.; Nofuji, Y.; Yonemoto, K.; Kumagai, S. Physical Fitness Measures as Potential Markers of Low Cognitive Function in Japanese Community-Dwelling Older Adults without Apparent Cognitive Problems. J. Sports Sci. Med. 2014, 13, 590–596. [Google Scholar]
  30. Tainaka, K.; Aoki, J. Fitness-related factors associated with changes in walking speed in elderly women. Jpn. J. Phys. Fit. Sports Med. 2002, 51, 245–251. (in Japanese). [Google Scholar] [CrossRef]
  31. Pai, Y.C.; Rogers, M.W. Control of body mass transfer as a function of speed of ascent in sit-to-stand. Med. Sci. Sports Exerc. 1990, 22, 378–384. [Google Scholar] [CrossRef]
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Figure 1. Recruitment of the healthy group and hip fracture group.
Figure 1. Recruitment of the healthy group and hip fracture group.
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Figure 2. Sensor attachment position and size.
Figure 2. Sensor attachment position and size.
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Figure 3. Motion signal. Specifically, sit-to-stand-to-sit motion definitions (A), maximum speed gait acceleration (B).
Figure 3. Motion signal. Specifically, sit-to-stand-to-sit motion definitions (A), maximum speed gait acceleration (B).
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Figure 4. Harmonic ratio and power spectrum entropy. Specifically, vertical acceleration of two sit-to-stand-to-stand-sit of healthy and hip fracture (A), amplitudes of the first twenty harmonics of healthy and hip fracture (B), normalized probability for entropy of healthy and hip fracture (C), calculate of harmonic ratio and entropy of healthy and hip fracture (D).
Figure 4. Harmonic ratio and power spectrum entropy. Specifically, vertical acceleration of two sit-to-stand-to-stand-sit of healthy and hip fracture (A), amplitudes of the first twenty harmonics of healthy and hip fracture (B), normalized probability for entropy of healthy and hip fracture (C), calculate of harmonic ratio and entropy of healthy and hip fracture (D).
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Table 1. Comparison of the baseline data among the participants.
Table 1. Comparison of the baseline data among the participants.
CharacteristicsHealthy Group (n = 18)Hip Fracture Group (n = 18)p-Value
Height (cm)154.1 ± 4.8150.7 ± 5.50.055
Age (years)63.1 ± 3.276.2 ± 7.8<0.001
Weight (kg)52.6 ± 9.045.3 ± 5.70.006
BMI (kg/m2)22.1 ± 3.120.0 ± 2.40.029
MMSE (points) 27.3 ± 2.3
Type of hip fracture
Trochanteric fracture after OLIF 7 (39%)
Subtrochanteric fracture after OLIF 2 (11%)
Neck fracture 9 (50%)
Type of surgery
OLIF 3 (33%)
BHA 3 (33%)
THA 3 (33%)
Laterality
Right 7 (39%)
Left 11 (61%)
Gait aid
None18 (100%)9 (50%)
Cane0 (0%)9 (50%)
Date of measurement (days after surgery)46.7 ± 10.7
Seat height (cm)38.4 ± 0.838.9 ± 1.10.10
Mean ± standard deviation. BMI: body mass index, MMSE: mini-mental state examination, OLIF: open reduction internal fixation, BHA: bipolar hemiarthroplasty, THA: total hip arthroplasty. Data are expressed as number (%).
Table 2. Comparison of 5STS and MGS between the hip fracture and healthy groups.
Table 2. Comparison of 5STS and MGS between the hip fracture and healthy groups.
Healthy GroupHip Fracture Groupp-Value †Effect Size, dp-Value ‡
5STS (s)8.75 ± 1.0512.17 ± 3.20<0.0011.430.754
MGS (m/s)1.93 ± 0.211.09 ± 0.27<0.0013.510.012
Mean ± standard deviation. 5STS: five sit-to-stand test, MGS: maximum gait speed. †: Comparison between the two groups, ‡: analysis of covariance.
Table 3. Comparison of HR estimated for STSF and STS2s motions.
Table 3. Comparison of HR estimated for STSF and STS2s motions.
HR
Healthy GroupHip Fracture Groupp-ValueEffect Size, d
STSF
ML1.88 ± 0.721.56 ± 0.260.0920.59
VT3.65 ± 1.742.67 ± 1.010.0470.69
AP3.26 ± 1.332.76 ± 0.880.4430.44
Triaxial2.53 ± 1.132.04 ± 0.630.4060.53
STS2s
ML2.18 ± 0.891.75 ± 0.740.1250.52
VT3.42 ± 1.542.58 ± 0.830.0490.69
AP2.68 ± 0.672.34 ± 0.720.1500.49
Triaxial2.69 ± 1.192.18 ± 0.730.1310.52
Mean ± standard deviation. HR: harmonic ratio, STSF: sit-to-stand-to-sit fast, STS2s: sit-to-stand and stand-to-sit for 2 s, ML: mediolateral, VT: vertical, AP: anterior–posterior.
Table 4. Comparison of PSE estimated for STSF and STS2s motions.
Table 4. Comparison of PSE estimated for STSF and STS2s motions.
PSE
Healthy GroupHip Fracture Groupp-ValueEffect Size, d
STSF
ML6.01 ± 0.225.94 ± 0.160.2970.35
VT5.78 ± 0.245.68 ± 0.160.1480.49
AP5.78 ± 0.165.61 ± 0.160.0021.10
Triaxial5.85 ± 0.215.68 ± 0.180.0150.85
STS2s
ML7.63 ± 0.317.82 ± 0.160.0350.74
VT7.46 ± 0.227.63 ± 0.160.0100.88
AP7.47 ± 0.157.61 ± 0.170.0140.86
Triaxial7.45 ± 0.257.66 ± 0.170.0030.97
Mean ± standard deviation. PSE: power spectrum entropy, STSF: sit-to-stand-to-sit fast STS2s: sit-to-stand and stand-to-sit for 2 s, ML: mediolateral, VT: vertical, AP: anterior–posterior.
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Shimamura, T.; Ishikawa, H.; Fujii, H.; Katoh, H. Smoothness Evaluation Indices during Sit-to-Stand-to-Sit Motions in Healthy Older Females and after Hip Fracture Using an Accelerometer: A Pilot Study. Geriatrics 2023, 8, 98. https://doi.org/10.3390/geriatrics8050098

AMA Style

Shimamura T, Ishikawa H, Fujii H, Katoh H. Smoothness Evaluation Indices during Sit-to-Stand-to-Sit Motions in Healthy Older Females and after Hip Fracture Using an Accelerometer: A Pilot Study. Geriatrics. 2023; 8(5):98. https://doi.org/10.3390/geriatrics8050098

Chicago/Turabian Style

Shimamura, Takeshi, Hitoshi Ishikawa, Hiromi Fujii, and Hiroshi Katoh. 2023. "Smoothness Evaluation Indices during Sit-to-Stand-to-Sit Motions in Healthy Older Females and after Hip Fracture Using an Accelerometer: A Pilot Study" Geriatrics 8, no. 5: 98. https://doi.org/10.3390/geriatrics8050098

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