Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial.

Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person's mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person's daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

BACKGROUND: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70-95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.

Physical performance in older age by sex and educational level: the HUNT Study.

BACKGROUND: Population-based studies on physical performance provide important information on older people's health but rarely include the oldest and least-healthy segment of the population. The aim of this study was to provide representative estimates of physical performance by age, sex, and educational level based on recent data from a population-based health study in Norway that includes older people with a wide range in age and function. METHODS: In the fourth wave of the Trøndelag Health Study (2017-2019), all participants aged 70 + were invited to an additional examination of physical performance assessed by the Short Physical Performance Battery (SPPB), either by attending a testing station or by visits from ambulatory teams. The distribution and variation in SPPB total and subscores, as well as gait speed, are presented by sex, age, and educational level. RESULTS: The SPPB was registered in 11,394 individuals; 54.8% were women; the age range was 70-105.4 years, with 1,891 persons aged 85 + . SPPB scores decreased by 0.27 points (men) and 0.33 points (women) for each year of age, and gait speed by 0.02 m/sec (men) and 0.03 m/sec (women). Using a frailty cut-off for gait speed at < 0.8 m/sec, the proportion of participants categorized as frail increased from 13.9% in the 70-74 years cohort to 73.9% in participants aged 85 + . Level of education [Formula: see text] 10 years corresponded to 6 years (men) and 4 years (women) earlier onset of frailty (SPPB [Formula: see text] 9) compared to education [Formula: see text] 14 years. CONCLUSION: We found that the SPPB captured a gradual decline and wide distribution in physical performance in old age. The results provide information about physical performance, health status, and risk profiles at a population level and can serve as reference data for clinicians, researchers, and healthcare planners.

Performance of machine learning models in estimation of ground reaction forces during balance exergaming.

BACKGROUND: Balance training exercise games (exergames) are a promising tool for reducing fall risk in elderly. Exergames can be used for in-home guided exercise, which greatly increases availability and facilitates independence. Providing biofeedback on weight-shifting during in-home balance exercise improves exercise efficiency, but suitable equipment for measuring weight-shifting is lacking. Exergames often use kinematic data as input for game control. Being able to useg such data to estimate weight-shifting would be a great advantage. Machine learning (ML) models have been shown to perform well in weight-shifting estimation in other settings. Therefore, the aim of this study was to investigate the performance of ML models in estimation of weight-shifting during exergaming using kinematic data. METHODS: Twelve healthy older adults (mean age 72 (± 4.2), 10 F) played a custom exergame that required repeated weight-shifts. Full-body 3D motion capture (3DMoCap) data and standard 2D digital video (2D-DV) was recorded. Weight shifting was directly measured by 3D ground reaction forces (GRF) from force plates, and estimated using a linear regression model, a long-short term memory (LSTM) model and a decision tree model (XGBoost). Performance was evaluated using coefficient of determination ([Formula: see text]) and root mean square error (RMSE). RESULTS: Results from estimation of GRF components using 3DMoCap data show a mean (± 1SD) RMSE (% total body weight, BW) of the vertical GRF component ([Formula: see text]) of 4.3 (2.5), 11.1 (4.5), and 11.0 (4.7) for LSTM, XGBoost and LinReg, respectively. Using 2D-DV data, LSTM and XGBoost achieve mean RMSE (± 1SD) in [Formula: see text] estimation of 10.7 (9.0) %BW and 19.8 (6.4) %BW, respectively. [Formula: see text] was [Formula: see text] for the LSTM in the [Formula: see text] component using 3DMoCap data, and [Formula: see text] using 2D-DV data. For XGBoost, [Formula: see text] [Formula: see text] was [Formula: see text] using 3DMoCap data, and [Formula: see text] using 2D-DV data. CONCLUSION: This study demonstrates that an LSTM model can estimate 3-dimensional GRF components using 2D kinematic data extracted from standard 2D digital video cameras. The [Formula: see text] component is estimated more accurately than [Formula: see text] and [Formula: see text] components, especially when using 2D-DV data. Weight-shifting performance during exergaming can thus be extracted using kinematic data only, which can enable effective independent in-home balance exergaming.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors.

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear.

Robustness of In-Laboratory and Daily-Life Gait Speed Measures over One Year in High Functioning 61- to 70-Year-Old Adults.

INTRODUCTION: Gait speed is a simple and safe measure with strong predictive value for negative health outcomes in clinical practice, yet in-laboratory gait speed seems not representative for daily-life gait speed. This study aimed to investigate the interrelation between and robustness of in-laboratory and daily-life gait speed measures over 12 months in 61- to 70-year-old adults. METHODS: Gait speed was assessed in laboratory through standardized stopwatch tests and in daily life by 7 days of trunk accelerometry in the PreventIT cohort, at baseline, and after 6 and 12 months. The interrelation was investigated using Pearson's correlations between gait speed measures at each time point. For robustness, changes over time and variance components were assessed by ANOVA and measurement agreement over time by Bland-Altman analyses. RESULTS: Included were 189 participants (median age 67 years [interquartile range: 64-68], 52.2% females). In-laboratory and daily-life gait speed measures showed low correlations (Pearson's r = 0.045-0.455) at each time point. Moreover, both in-laboratory and daily-life gait speed measures appeared robust over time, with comparable and smaller within-subject than between-subject variance (range 0.001-0.095 m/s and 0.032-0.397 m/s, respectively) and minimal differences between measurements over time (Bland-Altman) with wide limits of agreement (standard deviation of mean difference range: 0.12-0.34 m/s). DISCUSSION/CONCLUSION: In-laboratory and daily-life gait speed measures show robust assessments of gait speed over 12 months and are distinct constructs in this population of high-functioning adults. This suggests that (a combination of) both measures may have added value in predicting health outcomes.

Sensitivity to Change and Responsiveness of the Original and the Shortened Version of the Community Balance and Mobility Scale for Young Seniors.

OBJECTIVE: To examine sensitivity to change and responsiveness of the Community Balance and Mobility Scale (CBM) and shortened CBM (s-CBM). DESIGN: Secondary analysis using data of a randomized controlled trial. SETTING: General community. PARTICIPANTS: Young community-dwelling seniors aged 61-70 years (N=134; mean age, 66.2±2.5y). INTERVENTIONS: Participants underwent 12 months of exercise intervention. MAIN OUTCOME MEASURES: CBM and s-CBM. Sensitivity to change was assessed using standardized response mean (SRM) and paired t tests as appropriate. Responsiveness was assessed using 2 minimal important difference (MID) estimates. Analyses were conducted for the full sample and for the subgroups "high-balance" and "low-balance," divided by median split. RESULTS: Inferential statistics revealed a significant CBM (P<.001) and s-CBM (P<.001) improvement within the full sample and the subgroups (high-balance: P=.001, P=.019; low-balance: P<.001, P<.001). CBM and s-CBM were moderately sensitive to change (SRM, 0.48 vs 0.38) within the full sample. In the high-balance subgroup, moderate SRM values (0.70) were found for the CBM and small values for the s-CBM (0.29). In the low-balance subgroup, moderate SRM values were found for the CBM (0.67) and high values for the s-CBM (0.80). For the full sample, CBM and s-CBM exceeded the lower but not the higher MID value. In the high-balance subgroup, the CBM exceeded both MID values, but the s-CBM exceeded only the lower. In the low-balance subgroup, CBM and s-CBM exceeded both MID values. CONCLUSIONS: The CBM is a suitable tool to detect intervention-related changes of balance and mobility in young, high-performing seniors. Both versions of the CBM scale show good sensitivity to change and responsiveness, particularly in young seniors with low balance.

It's not about the capture, it's about what we can learn": a qualitative study of experts' opinions and experiences regarding the use of wearable sensors to measure gait and physical activity.

BACKGROUND: The use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants' opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity. METHODS: Twenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis. RESULTS: Five themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress. CONCLUSIONS: Multiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon's signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.

Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test.

Extensive test batteries are often needed to obtain a comprehensive picture of a person's functional status. Many test batteries are not suitable for active and healthy adults due to ceiling effects, or require a lot of space, time, and training. The Community Balance and Mobility Scale (CBMS) is considered a gold standard for this population, but the test is complex, as well as time- and resource intensive. There is a strong need for a faster, yet sensitive and robust test of physical function in seniors. We sought to investigate whether an instrumented Timed Up and Go (iTUG) could predict the CBMS score in 60 outpatients and healthy community-dwelling seniors, where features of the iTUG were predictive, and how the prediction of CBMS with the iTUG compared to standard clinical tests. A partial least squares regression analysis was used to identify latent components explaining variation in CBMS total score. The model with iTUG features was able to predict the CBMS total score with an accuracy of 85.2% (84.9-85.5%), while standard clinical tests predicted 82.5% (82.2-82.8%) of the score. These findings suggest that a fast and easily administered iTUG could be used to predict CBMS score, providing a valuable tool for research and clinical care.

The Adapted Lifestyle-Integrated Functional Exercise Program for Preventing Functional Decline in Young Seniors: Development and Initial Evaluation.

BACKGROUND: The Lifestyle-integrated Functional Exercise (LiFE) program is an intervention integrating balance and strength activities into daily life, effective at reducing falls in at-risk people ≥70 years. There is potential for LiFE to be adapted to young seniors in order to prevent age-related functional decline. OBJECTIVE: We aimed to (1) develop an intervention by adapting Lifestyle-integrated Functional Exercise (aLiFE) to be more challenging and suitable for preventing functional decline in young seniors in their 60s and (2) perform an initial feasibility evaluation of the program. Pre-post changes in balance, mobility, and physical activity (PA) were also explored. METHODS: Based on a conceptual framework, a multidisciplinary expert group developed an initial aLiFE version, including activities for improving strength, neuromotor performances, and PA. Proof-of-concept was evaluated in a 4-week pre-post intervention study measuring (1) feasibility including adherence, frequency of practice, adverse events, acceptability (i.e., perceived helpfulness, adaptability, level of difficulty of single activities), and safety, and (2) changes in balance/mobility (Community Balance and Mobility Scale) and PA (1 week activity monitoring). The program was refined based on the study results. RESULTS: To test the initial aLiFE version, 31 young seniors were enrolled and 30 completed the study (mean age 66.4 ± 2.7 years, 60% women). Of a maximum possible 16 activities, participants implemented on average 12.1 ± 1.8 activities during the intervention, corresponding to mean adherence of 76%. Implemented activities were practiced 3.6-6.1 days/week and 1.8-7.8 times/day, depending on the activity type. One noninjurious fall occurred during practice, although the participant continued the intervention. The majority found the activities helpful, adaptable to individual lifestyle, appropriately difficult, and safe. CMBS score increased with medium effect size (d = 0.72, p = 0.001). Increase in daily walking time (d = 0.36) and decrease in sedentary time (d = -0.10) were nonsignificant. Refinements included further increasing the task challenge of some strength activities and defining the most preferred activities in the trainer's manual to facilitate uptake of the program. CONCLUSION: aLiFE has the potential to engage young seniors in regular lifestyle-integrated activities. Effectiveness needs to be evaluated in a randomized controlled trial.

Performance-based clinical tests of balance and muscle strength used in young seniors: a systematic literature review.

BACKGROUND: Many balance and strength tests exist that have been designed for older seniors, often aged ≥70 years. To guide strategies for preventing functional decline, valid and reliable tests are needed to detect early signs of functional decline in young seniors. Currently, little is known about which tests are being used in young seniors and their methodological quality. This two-step review aims to 1) identify commonly used tests of balance and strength, and 2) evaluate their measurement properties in young seniors. METHODS: First, a systematic literature search was conducted in MEDLINE to identify primary studies that employed performance-based tests of balance and muscle strength, and which aspects of balance and strength these tests assess in young seniors aged 60-70. Subsequently, for tests used in ≥3 studies, a second search was performed to identify method studies evaluating their measurement properties. The quality of included method studies was evaluated using the Consensus-based Standards for selection of health Measurement Instruments (COSMIN) checklist. RESULTS: Of 3454 articles identified, 295 met the inclusion criteria. For the first objective, 69 balance and 51 muscle strength tests were identified, with variations in administration mode and outcome reporting. Twenty-six balance tests and 15 muscle strength tests were used in ≥3 studies, with proactive balance tests and functional muscle power tests used most often. For the second objective, the search revealed 1880 method studies, of which nine studies (using 5 balance tests and 1 strength test) were included for quality assessment. The Timed Up and Go test was evaluated the most (4 studies), while the Community Balance and Mobility (CBM) scale was the second most assessed test (3 studies). For strength, one study assessed the reliability of the Five times sit-to-stand. CONCLUSION: Commonly used balance and muscle strength tests in young seniors vary greatly with regards to administration mode and outcome reporting. Few studies have evaluated measurement properties of these tests when used in young seniors. There is a need for standardisation of existing tests to improve their informative value and comparability. For measuring balance, the CBM is a new and promising tool to detect even small balance deficits in balance in young seniors.

Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: pooled analysis of four European cohort studies.

BACKGROUND: Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65-75 years old. METHODS: Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65-75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts. RESULTS: Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts. CONCLUSIONS: Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65-75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings.

Use it or lose it? Effects of age, experience, and disuse on crawling.

What happens to early acquired but later abandoned motor skills? To investigate effects of disuse on early-developing motor skills, we examined crawling in two groups of habitual crawlers (34 6-12-month-old infants and five adults with Uner Tan Syndrome) and two groups of rusty crawlers (27 11-12-year-old children and 13 college-aged adults). Habitual crawlers showed striking similarities in gait patterns, limbs supporting the body, and crawling speed, despite dramatic differences in crawling practice, posture, and body size. Habitual crawlers trotted predominantly, whereas rusty crawlers showed a variety of gait patterns. Within sequences, habitual crawlers and children showed more switches in gait patterns than young adults. Children crawled faster and kept fewer limbs on the grounds than the other groups. Old crawling patterns were retained despite disuse, but new ones were also added. Surprisingly, results indicate that nothing was lost with disuse, but some features of crawling were gained or altered.

Predicting Trajectories of Functional Decline in 60- to 70-Year-Old People.

BACKGROUND: Early identification of people at risk of functional decline is essential for delivering targeted preventive interventions. OBJECTIVE: The aim of this study is to identify and predict trajectories of functional decline over 9 years in males and females aged 60-70 years. METHODS: We included 403 community-dwelling participants from the InCHIANTI study and 395 from the LASA study aged 60-70 years at baseline, of whom the majority reported no functional decline at baseline (median 0, interquartile range 0-1). Participants were included if they reported data on ≥2 measurements of functional ability during a 9-year follow-up. Functional ability was scored with 6 self-reported items on activities of daily living. We performed latent class growth analysis to identify trajectories of functional decline and applied multinomial regression models to develop prediction models of identified trajectories. Analyses were stratified for sex. RESULTS: Three distinct trajectories were identified: no/little decline (219 males, 241 females), intermediate decline (114 males, 158 females), and severe decline (36 males, 30 females). Higher gait speed showed decreased risk of functional limitations in males (intermediate limitations, odds ratio [OR] 0.74, 95% CI 0.57-0.97; severe limitations, OR 0.42, 95% CI 0.26-0.66). The final model in males further included the predictors fear of falling and alcohol intake (no/little decline, area under the receiver operating curve [AUC] 0.68, 95% CI 0.62-0.73; intermediate decline, AUC 0.63, 95% CI 0.56-0.69; severe decline, AUC 0.79, 95% CI 0.71-0.87). In females, higher gait speed showed a decreased risk of intermediate limitations (OR 0.51, 95% CI 0.38-0.68) and severe limitations (OR 0.18, 95% CI 0.07-0.44). Other predictors in females were age, living alone, economic satisfaction, balance, physical activity, BMI, and cardiovascular disease (no/little decline, AUC 0.80, 95% CI 0.75-0.85; intermediate decline, AUC 0.74, 95% CI 0.69-0.79; severe decline, AUC 0.95, 95% CI 0.91-0.99). CONCLUSION: Already in people aged 60-70 years, 3 distinct trajectories of functional decline were identified in these cohorts over a 9-year follow-up. Predictors of trajectories differed between males and females, except for gait speed. Identification of people at risk is the basis for targeting interventions.

Concurrent validity and reliability of the Community Balance and Mobility scale in young-older adults.

BACKGROUND: With the growing number of young-older adults (baby-boomers), there is an increasing demand for assessment tools specific for this population, which are able to detect subtle balance and mobility deficits. Various balance and mobility tests already exist, but suffer from ceiling effects in higher functioning older adults. A reliable and valid challenging balance and mobility test is critical to determine a young-older adult's balance and mobility performance and to timely initiate preventive interventions. The aim was to evaluate the concurrent validity, inter- and intrarater reliability, internal consistency, and ceiling effects of a challenging balance and mobility scale, the Community Balance and Mobility Scale (CBM), in young-older adults aged 60 to 70 years. METHODS: Fifty-one participants aged 66.4 ± 2.7 years (range, 60-70 years) were assessed with the CBM. The Fullerton Advanced Balance scale (FAB), 3-Meter Tandem Walk (3MTW), 8-level balance scale, Timed-Up-and-Go (TUG), and 7-m habitual gait speed were used to estimate concurrent validity, examined by Spearman correlation coefficient (ρ). Inter- and intrarater reliability were calculated as Intra-class-correlations (ICC), and internal consistency by Cronbach alpha and item-total correlations (ρ). Ceiling effects were determined by obtaining the percentage of participants reaching the highest possible score. RESULTS: The CBM significantly correlated with the FAB (ρ = 0.75; p < .001), 3MTW errors (ρ = - 0.61; p < .001), 3MTW time (ρ = - 0.35; p = .05), the 8-level balance scale (ρ = 0.35; p < .05), the TUG (ρ = - 0.42; p < .01), and 7-m habitual gait speed (ρ = 0.46, p < .001). Inter- (ICC2,k = 0.97), intrarater reliability (ICC3,k = 1.00) were excellent, and internal consistency (α = 0.88; ρ = 0.28-0.81) was good to satisfactory. The CBM did not show ceiling effects in contrast to other scales. CONCLUSIONS: Concurrent validity of the CBM was good when compared to the FAB and moderate to good when compared to other measures of balance and mobility. Based on this study, the CBM can be recommended to measure balance and mobility performance in the specific population of young-older adults. TRIAL REGISTRATION: Trial number: ISRCTN37750605 . (Registered on 21/04/2016).

Complexity of Daily Physical Activity Is More Sensitive Than Conventional Metrics to Assess Functional Change in Younger Older Adults.

The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60⁻70 years old) participated in a 4-week home-based exercise intervention. The Community Balance and Mobility Scale (CBMS) was used for clinical assessment of the participants’ functional balance and mobility performance pre- and post- intervention. Accelerometers worn on the low back were used to register PA of one week before and in the third week of the intervention. Changes in conventional univariate PA metrics (percentage of walking and sedentary time, step counts, mean cadence) and complexity were compared to the change as measured by the CBMS. Statistical analyses (21 participants) showed significant rank correlation between the change as measured by complexity and CBMS (ρ = 0.47, p = 0.03). Smoothing the activity output improved the correlation (ρ = 0.58, p = 0.01). In contrast, change in univariate PA metrics did not show correlations. These findings demonstrate the high potential of the complexity metric being useful and more sensitive than conventional PA metrics for assessing functional changes in younger older adults.