Aim
In order to account for the variability in gait speed due to demographic factors, an observed gait speed value can be compared with its predicted value based on age, sex, and body height ...(observed gait speed divided by predicted gait speed, termed “GS%predicted” henceforth). This study aimed to examine the screening accuracy of an optimal GS%predicted threshold for prefrailty/frailty.
Methods
This cross‐sectional study included 998 community‐dwelling ambulant participants aged >50 years (mean age = 68 years). Participants completed a multi‐domain geriatric screen and a physical fitness assessment, from which the 10‐m habitual gait speed, GS%predicted, Physical Frailty Phenotype (PFP) index, and 36‐item Frailty Index (FI) were computed.
Results
Based on the FI, ~49% of participants had pre‐frailty or frailty. The optimal threshold of GS%predicted (0.93) had greater screening accuracy than the 1.0 m/s fixed threshold for gait speed (AUC, 0.65 vs. 0.60; DeLong's P < 0.001). Replacing gait speed with GS%predicted in the PFP improved its overall discrimination (AUC, 0.70 vs. 0.67 of original PFP; DeLong's P < 0.001).
Conclusions
Defining a “slow” gait speed by a GS%predicted value of <0.93 provided greater screening accuracy than the traditional 1.0 m/s threshold for gait speed. Our results also support the use of GS%predicted‐derived PFP to identify older adults at risk of prefrailty/frailty. Geriatr Gerontol Int 2022; 22: 575–580.
To account for the variability in habitual gait speed due to demographic factors, an observed gait speed value can be compared with its predicted value based on age, sex, and body height (observed gait speed divided by predicted gait speed, termed “GS%predicted” henceforth). In a large sample of 998 community‐dwelling older adults, we found that defining a “slow” gait speed by a GS%predicted value of <0.93 provided greater prefrailty/frailty screening accuracy than the traditional 1.0 m/s threshold for gait speed.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This systematic review investigated the effects of high-intensity exercise (HIE) on lower limb (LL) function in acute and subacute stroke patients. A systematic electronic search was performed in ...PubMed, CINAHL and the Web of Science from inception to 30 June 2022. Outcomes examined included LL function and measures of activities of daily living such as the Barthel index, 6 min walk test (6MWT), gait speed and Berg balance scale (BBS), adverse events and safety outcomes. The methodological quality and the quality of evidence for each study was assessed using the PEDro scale and the Risk of Bias 2 tool (RoB 2). HIE was defined as achieving at least 60% of the heart rate reserve (HRR) or VO
peak, 70% of maximal heart rate (HR
), or attaining a score of 14 or more on the rate of perceived exertion Borg scale (6-20 rating scale). This study included randomized controlled trials (RCTs) which compared an intervention group of HIE to a control group of lower intensity exercise, or no intervention. All participants were in the acute (0-3 months) and subacute (3-6 months) stages of stroke recovery. Studies were excluded if they were not RCTs, included participants from a different stage of stroke recovery, or if the intervention did not meet the pre-defined HIE criteria. Overall, seven studies were included that used either high-intensity treadmill walking, stepping, cycling or overground walking exercises compared to either a low-intensity exercise (
= 4) or passive control condition (
= 3). Three studies reported significant improvements in 6MWT and gait speed performance, while only one showed improved BBS scores. No major adverse events were reported, although minor incidents were reported in only one study. This systematic review showed that HIE improved LL functional task performance, namely the 6MWT and gait speed. Previously, there was limited research demonstrating the efficacy of HIE early after stroke. This systematic review provides evidence that HIE may improve LL function with no significant adverse events report for stroke patients in their acute and subacute rehabilitation stages. Hence, HIE should be considered for implementation in this population, taking into account the possible benefits in terms of functional outcomes, as compared to lower intensity interventions.
Although the frailty index (FI) is designed as a continuous measure of frailty, thresholds are often needed to guide its interpretation. This study aimed to introduce and demonstrate the utility of ...an item response theory (IRT) method in estimating FI interpretation thresholds in community-dwelling adults and to compare them with cutoffs estimated using the receiver operating characteristics (ROC) method.
A sample of 1,149 community-dwelling adults (meanSD, 687 years) participated in this cross-sectional study. Participants completed a multi-domain geriatric screen from which the 40-item FI and 3 clinical anchors were computed - namely, (i)self-reported mobility limitations (SRML), (ii)"fair" or "poor" self-rated health (SRH), and (iii) restricted life-space mobility (RLSM). Participants were classified as having SRML-1 if they responded "Yes" to either of the 2 questions regarding walking and stair climbing difficulty and SRML-2 if they reported having walking and stair climbing difficulty. Participants with a Life Space Assessment score <60 points were classified as having RLSM. Threshold values for all anchor questions were estimated using the IRT method and ROC analysis with Youden criterion.
The proportions of participants with SRML-1, SRML-2, Fair/Poor SRH, and RLSM were 21 %, 8 %, 22 %, and 9 %, respectively. The IRT-based thresholds for SRML-2 (0.26), fair/poor SRH (0.29), and RLSM (0.32) were significantly higher than those for SRML-1 (0.18). ROC-based FI cutoffs were significantly lower than IRT-based values for SRML-2, SRH, and RLSM (0.12 to 0.17), and they varied minimally and non-systematically across the anchors.
The IRT method identifies biologically plausible FI thresholds that could meaningfully complement and contextualize existing thresholds for defining frailty.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose of Review
This study is aimed at describing and evaluating physical activity interventions in individuals that have undergone hip or knee joint replacement due to osteoarthritis.
Recent ...Findings
A total of 11,873 studies were screened. Seven studies with 627 participants, aged 50 to 85 years, met the review criteria. There are five randomised control trial, one longitudinal quasi-experimental study with a control group, and one pre-/post-test study with control group. Interventions included health coaching, a walking programme, a behavioural change intervention, and an alpine skiing intervention delivered between 6 and 24 weeks. Two studies reported change in physical activity using patient activity diaries and five used objective accelerometer data. All studies showed an increase in time spent being physically active in the intervention groups. One study also reported an increase in vitality.
Summary
Few studies have investigated physical activity interventions after hip or knee joint replacement, and evidence for the effectiveness of physical activity interventions post-replacement is low. High-quality studies are needed in this area to explore the potential benefits presented within this review.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•The adjusted predictive modeling (APM) method estimates interpretational thresholds.•The APM method generates biologically plausible cutpoints for mobility difficulty.•The APM-based cutpoints are ...more precise than ROC-based cutpoints.•The APM method uses common regression methods and can be easily applied.
Clinical interpretability of the gait speed and 5-times sit-to-stand (5-STS) tests is commonly established by comparing older adults with and without self-reported mobility limitations (SRML) on gait speed and 5-STS performance, and estimating clinical cutpoints for SRML using the receiver operating characteristics (ROC) method. Accumulating evidence, however, suggests that the adjusted predictive modeling (APM) method may be more appropriate to estimate these interpretational cutpoints. Thus, we aimed to compare, in community-dwelling older adults, gait speed and 5-STS cutpoints estimated using the ROC and APM methods.
Cross-sectional study.
This study analyzed data from 955 community-dwelling independently walking older adults (73%women) aged ≥60 years (mean, 68; range, 60–88).
Participants completed the 10-metre gait speed and 5-STS tests. Participants were classified as having SRML if they responded "Yes" to either of the 2 questions regarding walking and stair climbing difficulty. Cutpoints for SRML and its component questions were estimated using ROC analysis with Youden criterion and the APM method.
The proportions of participants with self-reported walking difficulty, self-reported stair climbing difficulty, and SRML were 10%, 19%, and 22%, respectively. Gait speed and 5-STS time were moderately correlated with each other (r=-0.56) and with the self-reported measures (absolute r-values, 0.39–0.44). ROC-based gait speed cutpoints were 0.14 to 0.16 m/s greater than APM-based cutpoints (P < 0.05) whilst ROC-based 5-STS time cutpoints were 0.8 to 3.3 s lower than APM-based cutpoints (P < 0.05 for walking difficulty). Compared with ROC-based cutpoints, APM-based cutptoints were more precise and they varied monotonically with self-reported walking difficulty, self-reported stair climbing difficulty, and SRML.
In a sample of 955 older adults, our findings of precise and biologically plausible gait speed and 5-STS cutpoints for SRML estimated using the APM method indicate that this promising method could potentially complement or even replace traditional ROC methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Frailty begins in middle life and manifests as a decline in functional fitness. We described a model for community frailty screening and factors associated with prefrailty and frailty and fitness ...measures to distinguish prefrail/frail from robust older adults. We also compared the Fatigue, Resistance, Ambulation, Illnesses and Loss of weight (FRAIL) scale against Fried frailty phenotype and Frailty Index (FI).
Community-dwelling adults ≥55 years old were designated robust, prefrail or frail using FRAIL. The multidomain geriatric screen included social profiling and cognitive, psychological and nutritional assessments. Physical fitness assessments included flexibility, grip strength, upper limb dexterity, lower body strength and power, tandem and dynamic balance and cardiorespiratory endurance.
In 135 subjects, 99 (73.3%) were robust, 34 (25.2%) were prefrail and 2 (1.5%) were frail. After adjusting for age and sex, depression (odds ratio OR, 2.90; 95% confidence interval CI, 1.05-7.90;
= 0.040) and malnutrition (OR, 6.07; 95% CI, 2.52-14.64;
<0.001) were independently associated with prefrailty/frailty. Prefrail/frail participants had significantly poorer performance in upper limb dexterity (
= 0.030), lower limb power (
= 0.003), tandem and dynamic balance (
= 0.031) and endurance (
= 0.006). Except for balance and flexibility, all fitness measures differentiated prefrail/frail from robust women. In men, only lower body strength was significantly associated with frailty. Area under receiver operating characteristic curves for FRAIL against FI and Fried were 0.808 (0.688-0.927,
<0.001) and 0.645 (0.546-0.744,
= 0.005), respectively.
Mood and nutrition are targets in frailty prevention. Physical fitness declines early in frailty and manifests differentially in both genders.
Background
The differential risk profiles associated with prefrailty may be attributable to underlying intrinsic capacity (IC).
Objectives
We examine (i) effect of a multi-domain physical exercise ...and nutrition intervention on pre-frailty reversal in community-dwelling older adults at 1-year, and (ii) whether IC contributes to pre-frailty reversal.
Methods
Prefrail participants in this non-randomized study were invited to attend a 4-month exercise and nutritional intervention following a frailty screen in the community. Prefrailty was operationalized as (i) FRAIL score 1–2 or (ii) 0 positive response on FRAIL but with weak grip strength or slow gait speed based on the Asian Working Group for Sarcopenia cut-offs. Participants who fulfilled operational criteria for prefrailty but declined enrolment in the intervention programme served as the control group. All participants completed baseline IC assessment: locomotion (Short Physical Performance Battery, 6-minute walk test), vitality (nutritional status, muscle mass), sensory (self-reported hearing and vision), cognition (self-reported memory, age- and education adjusted cognitive performance), psychological (Geriatric Depression Scale-15, self-reported anxiety/ depression). Reversal of prefrailty was defined as achieving a FRAIL score of 0, with unimpaired grip strength and gait speed at 1-year follow-up.
Results
Of 81 participants (70.0 ± 6.6 years, 79.0% female), 52 participants (64.2%) were enrolled in the multi-domain intervention, and 29 participants (35.8%) who declined intervention constituted the control group. There was no difference in age, gender and baseline composite IC between groups. Reversal to robustness at 1-year was similar between intervention and control groups (30.8% vs. 44.8% respectively,
p
= 0.206). Reduced prevalence of depression was observed among participants in the intervention group at 1-year relative to baseline (7.8% vs. 23.1%,
p
= 0.022). In multiple logistic regression, intervention had no effect on prefrailty reversal, while higher composite IC exhibited reduced likelihood of remaining prefrail at 1-year (OR = 0.67, 95% CI 0.45–1.00,
p
= 0.049).
Conclusion
Focusing only on the locomotion and vitality domains through a combined exercise and nutritional intervention may not adequately address component domain losses to optimize prefrailty reversal. Future studies should examine whether an IC-guided approach to target identified domain declines may be more effective in preventing frailty progression.
Handgrip strength is commonly normalized or stratified by body size to define subgroup-specific cut-points and reference limits values. However, it remains unclear which anthropometric variable is ...most strongly associated with handgrip strength. We aimed to, in older adults with no self-reported mobility limitations, determine whether height, weight, and body mass index (BMI) were meaningfully associated with handgrip strength.
This cross-sectional study included community-dwelling ambulant participants, and we identified 775 older adults who reported no difficulty walking 100 m, climbing stairs, and rising from the chair. Handgrip strength was measured with a digital dynamometer. Bayesian linear regression was used to estimate the probabilities that the positive associations of height, weight, and BMI with handgrip strength exceeded 0 kg (the null value) and 2.5 kg (the clinically meaningful threshold value).
Mean handgrip strength was 22.1 kg (SD, 4) for women and 32.9 kg (SD, 6) for men. Body height, weight, and BMI had >99.9% probabilities of a positive association with handgrip strength; however, the associations of per interquartile increase in body weight and BMI with handgrip strength had low probabilities (<5%) of exceeding the clinically meaningful threshold of 2.5 kg. In contrast, body height had the highest probability (99.6%) of a clinically meaningful association with handgrip strength: adjusting for age and gender, handgrip strength was 3.2 kg (95% CrI, 2.7 to 3.8) greater in older adults 1.61 m tall than in older adults 1.51 m tall.
In a large sample of mobile-intact older adults, handgrip strength differed meaningfully by body height. Although requiring validation, our findings suggest that future efforts should be directed at normalizing handgrip strength by body height to better define subgroup-specific handgrip weakness. A web-based application (https://sghpt.shinyapps.io/ippts/) was created to allow interactive exploration of predicted values and reference limits of age-, gender-, and height-subgroups.
•Handgrip strength differed by body height, body weight, and body mass index (BMI) in community-dwelling older adults.•Only the association between body height and handgrip strength had a high probability of clinical significance.•Future studies should normalize handgrip strength by body height to better define subgroup-specific handgrip weakness.•Encouraging future work to evaluate clinical significance could facilitate practice standardization and harmonization.
Summary: In 775 community-dwelling older adults with no self-reported mobility limitations, we found that the association between body height and handgrip strength had a high probability of clinical significance, thereby shedding light on the important but previously unaddressed question of which anthropometric variables – namely, body height, weight, and BMI – were meaningfully associated with handgrip strength.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived ...cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.
A sample of 998 community-dwelling older adults (mean SD, 68 7 years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI ("model-based PFP"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.
The model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R
, 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's k
, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined "prefrail/frail" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.
The developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.
Paracetamol/Orphenadrine is a fixed dose combination containing 35 mg orphenadrine and 450 mg paracetamol. It has analgesic and muscle relaxant properties and is widely available as generics. This ...study is conducted to investigate the relative bioavailability and bioequivalence between one fixed dose paracetamol/orphenadrine combination test preparation and one fixed dose paracetamol/orphenadrine combination reference preparation in healthy volunteers under fasted condition for marketing authorization in Malaysia.
This is a single-center, single-dose, open-label, randomized, 2-treatment, 2-sequence and 2-period crossover study with a washout period of 7 days. Paracetamol/Orphenadrine tablets were administered after a 10-h fast. Blood samples for pharmacokinetic analysis were collected at scheduled time intervals prior to and up to 72 h after dosing. Blood samples were centrifuged, and separated plasma were kept frozen (- 15 °C to - 25 °C) until analysis. Plasma concentrations of orphenadrine and paracetamol were quantified using liquid-chromatography-tandem mass spectrometer using diphenhydramine as internal standard. The pharmacokinetic parameters AUC
, AUC
and C
were determined using plasma concentration time profile for both preparations. Bioequivalence was assessed according to the ASEAN guideline acceptance criteria for bioequivalence which is the 90% confidence intervals of AUC
, AUC
and C
ratio must be within the range of 80.00-125.00%.
There were 28 healthy subjects enrolled, and 27 subjects completed this trial. There were no significant differences observed between the AUC
, AUC
and C
of both test and reference preparations in fasted condition. The 90% confidence intervals for the ratio of AUC
(100.92-111.27%), AUC
(96.94-108.08%) and C
(100.11-112.50%) for orphenadrine (n = 25); and AUC
(94.29-101.83%), AUC
(94.77-101.68%) and C
(87.12-101.20%) for paracetamol (n = 27) for test preparation over reference preparation were all within acceptable bioequivalence range of 80.00-125.00%.
The test preparation is bioequivalent to the reference preparation and can be used interchangeably.
NMRR- 17-1266-36,001; registered and approved on 12 September 2017.
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