The influence of different horseshoes and ground substrates on mid-stance hoof orientation at the walk

BACKGROUND

Horseshoes with modified contact surfaces combined with deformable ground substrates are used to change hoof orientation during mid-stance, for example, for therapeutic reasons.

OBJECTIVES

To measure the effect of horseshoes and ground substrates on sagittal and transverse plane hoof orientation at mid-stance using a dorsal hoof wall mounted triaxial accelerometer.

STUDY DESIGN

In vivo experiment, randomised crossover design.

METHODS

Differences in sagittal and transverse plane angles between standing and mid-stance of the left front hoof of six horses walking with regular horseshoes, egg bar, toe-wide, medial-wide, lateral-wide and three-degree egg bar shoes on turf, sand and hard ground substrates were assessed with linear mixed models with horseshoe and substrate type as fixed factors (p < 0.05) for each animal.

RESULTS

Hoof angles were significantly affected by horseshoe (p < 0.001), surface (p < 0.001) and the combination (p < 0.001). The sagittal plane angle increased in deformable ground substrates at walk-in mid-stance on turf [mean (±standard deviation): 2.6° (±3.8°)] and on sand [2.6° (±4.1°)] across all shoes. The greatest increase was observed with egg bar shoes [turf: 4.37° (±3.82°); sand 4.69° (±3.83°)]. There was a tendency for the hoof to sink laterally into deformable ground substrates among all shoes [turf: 1.11° (±1.49°); sand: 0.93° (±1.93°)]. Medial-wide shoes increased the lateral sinking [turf: 2.00° (±1.63°); sand: 1.79° (±1.58°)]. Lateral-wide shoes reduced the lateral sinking on turf [0.62° (±1.26°)] and induced a marginal medial sinking on sand [-0.007° (±2.03°)].

MAIN LIMITATIONS

The substrate properties were not quantitatively assessed, and observations were limited to front hooves at the walk. A larger sample size would be preferable.

CONCLUSIONS

Mid-stance hoof orientation changes with specific combinations of shoes and ground substrates in the walking horse.

An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial

BACKGROUND

Numerous smartphone apps are targeting physical activity (PA) and healthy eating (HE), but empirical evidence on their effectiveness for the initialization and maintenance of behavior change, especially in children and adolescents, is still limited. Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps.

OBJECTIVE

The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family-based setting.

METHODS

A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (t0) and postintervention (t1) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between t0 and t1, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following t1, a follow-up assessment (t2) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2).

RESULTS

Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before t0. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all P>.30).

CONCLUSIONS

Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. Further research is needed to better understand intervention engagement and tailor mHealth interventions to individuals for enhanced effectiveness in primary prevention efforts.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00010415; https://drks.de/search/en/trial/DRKS00010415.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/20534.

An intraoperative accelerometry and real-time analysis tool for magnetic resonance-guided focused ultrasound thalamotomy

Magnetic resonance-guided focused ultrasound (MRgFUS) is one of the newest surgical treatments for essential tremor (ET). During this procedure, a lesion is created within the thalamus to mitigate tremor. Targeting is done using a combination of stereotaxy, MR tractography, and sublesional heating, with tremor assessed during the procedure to gauge therapeutic effectiveness. Currently, tremor assessments are done qualitatively, but this approach requires the tremor change to be above a subjective threshold and provides no objective record of surgical tremor progression. Here, the authors present and demonstrate an MR-compatible accelerometer with custom MATLAB analysis code and graphical user interface to record, visualize, and quantify tremor in near real-time. Results can be exported and saved for future review. This method was used in 20 surgeries, with patients experiencing a 50.7% (95% CI -64.1% to -37.3%) improvement in the treated limb per the Clinical Rating Scale for Tremor. This method does not interrupt the surgery and is quantitative. As research on optimizing MRgFUS treatment for ET continues-for example, the refinement of targeting during sublesional sonications-such quantifying and recording of tremor changes will provide rapid and objective feedback.

Associations between physical activity, sedentary behaviour and cognitive domain performance of people living with mild cognitive impairment in the community

INTRODUCTION

Physical activity is known to positively influence cognitive performance. For adults with mild cognitive impairment (MCI), the relationship between physical activity levels and cognitive performance is unknown. This cross-sectional study aimed to determine if cognitive performance [as measured by the Montreal Cognitive Assessment (MoCA)] of people living in the community with MCI is associated with their physical activity levels or sedentary behaviour.

METHODS

ActivPAL™ accelerometers were used to objectively measure physical activity and sedentary behaviour for seven full days. Cognitive performance was measured using the MoCA.

CONSUMER AND COMMUNITY INVOLVEMENT

No involvement other than as research participants RESULTS: Eighty-two participants from the Balance on the Brain randomised controlled trial were included. Most participants were retired (88%), with 33 (40%) reporting a fall in the last year. The median MoCA score was 24 (IQR 22-26). Participants achieved a mean of 6296 (±2420) steps per day and were sedentary for 10.6 (±2) hours per day. The only physical activity outcomes that had a fair, positive correlation were moderate- to vigorous-intensity physical activity measures of total stepping time and total number of steps (with a cadence of ≥100 steps/min) with the orientation MoCA domain score (r(82) = 0.36, p ≤ 0.001 and r(82) = 0.37, p ≤ 0.001, respectively). Higher total sedentary time had a weak, positive correlation with better visuospatial/executive performance (r(82) = 0.23, p = 0.041). The orientation outcomes remained significant when analysed in an adjusted logistic regression model.

CONCLUSION

This study found that performance in the MoCA orientation domain had a fair-positive correlation with moderate-intensity physical activity (i.e., stepping time and step count with a cadence of ≥100 steps/min) as measured by a thigh-worn accelerometer for community-dwelling older adults with MCI. When considering the relationship between cognitive domains and sedentary behaviour, consideration may be needed regarding whether cognitive enhancing activities (such as crosswords and other brain games) are being performed, which may confound this relationship. Further investigation is required to confirm these results.

Automatic detection of skate strokes in short-track speed skating using one single IMU: validation of a new method

Greater impulse is a key performance indicator of success in short track speed skating. The main objective of this study was to develop a method to measure skating strokes using a single IMU. Eight elite or world-class speed skaters had one IMU placed against their skin on the lower back, and a camera setup was positioned to capture the test. A maximal speed trial was then executed by each participant, and the data were analysed to estimate agreement between the camera and IMU estimates of skate stroke events. Inter-evaluator reliability was assessed on a dataset of 22 athletes performing speed trials as well. The algorithm detected 100% of the strokes identified on the video capture system with a root mean square error of 0.06s. Bland-Altman analysis showed a bias of 0.03s between the two methods, which corresponds to the frame rate of the camera. The inter-evaluator reliability yielded an intra-class correlation of 1.00 (ICC3,1) from a dataset of 7089 strokes. This study provides an example of on-ice evaluation of speed skating strokes using a single IMU. This equipment is less expensive than that employed by previous authors and can be implemented in training situations with low invasiveness.

Harmonization of three different accelerometers to classify the 24 h activity cycle

Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.

Association between musculoskeletal pain and exposures to awkward postures during work: a compositional analysis approach

OBJECTIVES

This study aimed to explore the association between arm elevation and neck/shoulder pain, and trunk forwarding bending and low back pain among home care workers.

METHODS

Home care workers (N = 116) from 11 home care units in Trondheim, Norway, filled in pain assessment and working hours questionnaire, and wore 3 accelerometers for up to 7 consecutive days. Work time was partitioned into upright awkward posture, nonawkward posture, and nonupright time, i.e. sitting. Within a compositional approach framework, posture time compositions were expressed in terms of log-ratio coordinates for statistical analysis and modeling. Poisson generalized linear mixed models were used to analyze the relationship between arm elevation in upright postures and neck/shoulder pain, and between trunk forward bending in upright postures and low back pain, respectively. Isotemporal substitution analysis was used to investigate the association of pain assessment with the reallocation of time spent in the different postures.

RESULTS

Time spent in awkward postures was modest, especially for the more extreme angles (60° and 90°). Adjusting for age, gender, and body mass index, our study suggested that the compositions of time spent by home care workers in awkward postures were significantly associated with pain assessment (P < 0.01). Isotemporal substitution analysis showed that reallocating 5 min from upright posture with arms elevated below to above 60° and 90° was associated with a 6.8% and 19.9% increase in the neck/shoulder pain score, respectively. Reallocating 5 min from a forward bending posture while upright below to above 30°, 60°, and 90° was associated with 1.8%, 3.5%, and 4.0% increase in low back pain, respectively.

CONCLUSIONS

Although the exposure to awkward postures was modest, our results showed an association between increased time spent in awkward postures and an increase in neck/shoulder pain and low back pain in home care workers. As musculoskeletal pain is the leading cause of sickness absence, these findings suggest that home care units could benefit from re-organizing work to avoid excessive arm elevation and trunk forward bending in workers.

KARATECH: A Practice Support System Using an Accelerometer to Reduce the Preliminary Actions of Karate

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

Association Between Sleep Efficiency Variability and Cognition Among Older Adults: Cross-Sectional Accelerometer Study

Background

Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established.

Objective

We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests.

Methods

We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes.

Results

A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores.

Conclusions

Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

Valitrack study: Objective evaluation of medical compression wearing time and patient activity with the Accutrack® by Sigvaris Group sensor

INTRODUCTION

Objective measurement of compliance is mandatory to evaluate therapeutic effects and clinical benefits of medical compression stockings. This Valitrack study evaluates the Accutrack sensor equipped with an accelerometer. The evaluation is done under controlled and free conditions and during washing.

RESULTS

Global accordance results are excellent: Sensitivity (98.6%) and specificity (95.2%) and 92% of patients are not disturbed by the sensors when putting on the stockings.

DISCUSSION

Thanks to its specificities one sensor per patient is sufficient. Its sensitivity distinguishes still and active positions. Only hand washing remains a false positive. The Accutrack sensor objectively measures compliance and will not be a bias because 96% of patients forget it.

CONCLUSIONS

Objective compliance measurement with Accutrack allows us to evaluate the efficacity of compression, the reasons of non-compliance, and the patient's actual physical activity according to its accelerometer time-stamped.

Influence of keel impacts and laying hen behavior on keel bone damage

Keel bone damage, which presents as fractures and/or deviations of the keel, has been detected in laying hens housed in all types of systems. Factors leading to keel bone damage in hens housed with limited vertical space, such as those housed in furnished systems, are not well understood, and are the topic of this study. Ten focal hens from each of 12 furnished cages (4 rooms of 3 cages) were fitted with keel mounted tri-axial accelerometers. Their behavior was video recorded continuously over two 3-wk trials: the first when the hens were between 52 and 60 wk of age, and the second approximately 20 wk later. The integrity of each hen's keel was evaluated at the start and end of each 3-wk trial using digital computed tomography. We identified predominant behaviors associated with acceleration events sustained at the keel (collisions, aggressive interactions and grooming) by pairing accelerometer outputs with video data. For each recorded acceleration event we calculated the acceleration magnitudes as the maximum summed acceleration recorded during the event, and by calculating the area under the acceleration curve. A principle components analysis, which was used as a data reduction technique, resulted in the identification of 4 components that were used in a subsequent regression analysis. A key finding is that the number of collisions a hen has with structures in her environment, and the number of aggressive interactions that a hen is involved, each affect the likelihood that she will develop 1 or more fractures within a 3-wk time span. This relationship between hen behavior and keel fracture formation was independent of the magnitude of acceleration involved in the event. Observed behavior did not have an impact on the formation of keel bone deviations, further supporting reports that the mechanisms underlying the 2 types of keel bone damage are different.

Better position for the wearable sensor to monitor badminton sport training loads

This study purposed to identify the better accelerometer location for monitoring badminton sports training load by investigating the correlation between internal (HR, training impulse [TRIMP]) training load and external (acceleration, player load [PL]) training loads measured from 5 different body locations. Twelve college-level badminton athletes wore a HR transmitter belt and five accelerometers fixed on both hands, legs, and lower back while performing continuous three-minute training of four badminton skills including backhand serve (BS), net shot (NS), footwork training (FT), and jump smash (JS). Results showed PL at the five locations were significantly related to TRIMP (r = 0.570-0.843, p < 0.05), in which PL at lower back has highest Pearson (r = 0.843) and partial (r = 0.366) correlated to TRIMP (p < 0.05). Base on the stepwise multiple regression, PL at lower back and racket hand explained 88% of the variance of TRIMP (R2 = 0.879). In conclusion, lower back is an idea location for accelerometer to monitor overall external training load in badminton if considering one accelerometer location, while combining PL at the lower back and racket hand can predict 88% of the variation of the internal training load.

Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = -0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture.

Evaluation of sensor-based health monitoring in dairy cows: Exploiting rumination times for health alerts around parturition

The use of sensor-based measures of rumination time as a parameter for early disease detection has received significant attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOPs). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach according to the SOPs was implemented. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on the status of the health alerts and their health status, to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, a SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition.

Feature evaluation of accelerometry signals for cough detection

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector.

Maximum a posteriori detection of heartbeats from a chest-worn accelerometer

Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.

Physical Activity Patterns Within Dementia Care Dyads

Previous research has explored the physical activity habits of people with dementia and their family carers separately, with little consideration of how physical habits are associated within dyads. In this observational study, we sought to explore the relationship between people with dementia and their carers' physical activity, at a group level and at a dyadic level. Twenty-six participant dyads (persons with dementia and their carer spouses) were asked to wear an accelerometer for 30 days continuously. Comparisons were made at a group level and a dyadic level. People with dementia did not participate in significantly more moderate to vigorous physical activity (M = 15.44 min/day; SD = 14.40) compared with carers (M = 17.95 min/day; SD = 17.01). Within dyads, there were moderately strong associations between daily moderate to vigorous physical activity (r = .48-.54), but not with overall activity levels (r = .24). Despite physical activity habits remaining relatively low within people with dementia and carers, respectively, moderate to vigorous physical activity levels appear to be correlated within dyads. Understanding mutual influence on physical activity levels within dyads is an important pathway to promote an active lifestyle.

High Signal-to-Noise Ratio Event-Driven MEMS Motion Sensing

Two solutions for improving MEMS triboelectric vibration sensors performance in contact-separation mode are reported experimentally and analytically. Triboelectric sensors have mostly been studied in the mesoscale. The gap variation between the electrodes induces a potential difference that represents the external vibration. Miniaturizing the device limits the sensor output because of the limited gap. This work offers a warped MEMS diaphragm constrained on its edges. The dome-shaped structure provides one order of magnitude larger displacement after contact-separation than standard designs resulting in one order of magnitude greater voltage and signal-to-noise-ratio. Second, micro triboelectric sensors do not operate unless the external vibration is sufficiently forceful to initiate contact between layers. The proposed constraints on the edge of the diaphragm provide friction during periodic motion and generate charges. The combination of the warped diaphragm and boundary constraints instead of serpentine springs increases the charge density and voltage generation. The mechanical properties and electrical output are thoroughly investigated including nonlinearity, sensitivity, and signal-to-noise ratio. A sensitivity of 250 mV g-1 and signal-to-noise-ratio of 32 dB is provided by the presented device at resonance, which is very promising for event-driven motion sensors because it does not require signal conditioning and therefore simplifies the sensing circuitry.

Differences in physical activity volume and intensity between Unified and non-Unified Special Olympics football training: An observational pilot study

BACKGROUND

The participation of people with intellectual disabilities in Special Olympics sports and training opportunities offers numerous benefits for health and inclusion. However, little is known about the impact of such training on physical activity behaviour. Here, we evaluate the differences in physical activity volume and intensity of Special Olympics athletes between Unified and non-Unified football training.

METHOD

Accelerometer data of 12 male athletes from eight standardised training sessions (four Unified, four non-Unified) were analysed.

RESULTS

While there was no statistically significant difference for the main part of the training, athletes showed higher levels of physical activity intensity (MVPA: Mdiff  = 11.74%; 95% CI = 5.50-17.97) and volume (average acceleration ENMO: Mdiff  = 112.82 mg; 95% CI = 24.73-200.90) in a Unified compared to non-Unified endurance-related exercise task.

CONCLUSION

Understanding physical activity participation in different training types can help to design and implement future training programmes.

Follow-up of individualised physical activity on prescription and individualised advice in patients with hip or knee osteoarthritis: A randomised controlled trial

OBJECTIVE

Compare the long-term effects of two different individualised physical activity interventions in hip or knee osteoarthritis patients.

DESIGN

Randomised, assessor-blinded, controlled trial.

SETTING

Primary care.

SUBJECTS

Patients with clinically verified hip or knee osteoarthritis, <150 min/week with moderate or vigorous physical activity, aged 40-74.

INTERVENTION

The advice group (n = 69) received a 1-h information and goalsetting session for individualised physical activity. The prescription group (n = 72) received information, goalsetting, individualised written prescription, self-monitoring, and four follow-ups.

MAIN MEASURES

Physical activity, physical function, pain and quality of life at baseline, 6, 12 and 24 months.

RESULTS

There were only minor differences in outcomes between the two groups. For self-reported physical activity, the advice group had improved from a mean of 102 (95% CI 74-130) minutes/week at baseline to 214 (95% CI 183-245) minutes/week at 24 months, while the prescription group had improved from 130 (95% CI 103-157) to 176 (95% CI 145-207) minutes/week (p = 0.01 between groups). Number of steps/day decreased by -514 (95% CI -567-462) steps from baseline to 24 months in the advice group, and the decrease in the prescription group was -852 (95% CI -900-804) steps (p = 0.415 between groups). Pain (HOOS/KOOS) in the advice group had improved by 7.9 points (95% CI 7.5-8.2) and in the prescription group by 14.7 points (95% CI 14.3-15.1) from baseline to 24 months (p = 0.024 between groups).

CONCLUSIONS

There is no evidence that individualised physical activity on prescription differs from individualised advice in improving long-term effects in patients with hip or knee osteoarthritis.

A systematic review of the performance of actigraphy in measuring sleep stages

The accuracy of actigraphy for sleep staging is assumed to be poor, but examination is limited. This systematic review aimed to assess the performance of actigraphy in sleep stage classification of adults. A systematic search was performed using MEDLINE, Web of Science, Google Scholar, and Embase databases. We identified eight studies that compared sleep architecture estimates between wrist-worn actigraphy and polysomnography. Large heterogeneity was found with respect to how sleep stages were grouped, and the choice of metrics used to evaluate performance. Quantitative synthesis was not possible, so we performed a narrative synthesis of the literature. From the limited number of studies, we found that actigraphy-based sleep staging had some ability to classify different sleep stages compared with polysomnography.

Comprehensive Noise Modeling of Piezoelectric Charge Accelerometer with Signal Conditioning Circuit

This paper reports on noise modeling of a piezoelectric charge accelerometer with a signal conditioning circuit. The charge output is converted into voltage and amplified using a JFET operational amplifier that has high input resistance and low noise. The noise sources in the whole system include electrical and mechanical thermal noises of the accelerometer, thermal noises of resistors, and voltage and current noises of the operational amplifier. Noise gain of each source is derived from small signal circuit analysis. It is found that the feedback resistor of the operational amplifier is a major source of noise in low frequencies, whereas electrical thermal noise of the accelerometer dominates the rest of spectrum. This method can be used to pair a highly sensitive sensor with a single JFET operational amplifier instead of a multi-stage signal conditioning circuit.

Development of a Parturition Detection System for Korean Native Black Goats

Korean Native Black Goats deliver mainly during the cold season. However, in winter, there is a high risk of stunted growth and mortality for their newborns. Therefore, we conducted this study to develop a KNBG parturition detection system that detects and provides managers with early notification of the signs of parturition. The KNBG parturition detection system consists of triaxial accelerometers, gateways, a server, and parturition detection alarm terminals. Then, two different data, the labor and non-labor data, were acquired and a Decision Tree algorithm was used to classify them. After classifying the labor and non-labor states, the sum of the labor status data was multiplied by the activity count value to enhance the classification accuracy. Finally, the Labor Pain Index (LPI) was derived. Based on the LPI, the optimal processing time window was determined to be 10 min, and the threshold value for labor classification was determined to be 14 240.92. The parturition detection rate was 82.4%, with 14 out of 17 parturitions successfully detected, and the average parturition detection time was 90.6 min before the actual parturition time of the first kid. The KNBG parturition detection system is expected to reduce the risk of stunted growth and mortality due to hypothermia in KNBG kids by detecting parturition 90.6 min before the parturition of the first kid, with a success rate of 82.4%, enabling parturition nursing.

Investigation of New Accelerometer Based on Capacitive Micromachined Ultrasonic Transducer (CMUT) with Ring-Perforation Membrane

Capacitive micromachined ultrasonic transducer (CMUT) has been widely studied due to its excellent resonance characteristics and array integration. This paper presents the first study of the CMUT electrostatic stiffness resonant accelerometer. To improve the sensitivity of the CMUT accelerometer, this paper innovatively proposes the CMUT ring-perforation membrane structure, which effectively improves the acceleration sensitivity by reducing the mechanical stiffness of the elastic membrane. The acceleration sensitivity is 10.9 (Hz/g) in the acceleration range of 0-20 g, which is 100% higher than that of the conventional CMUT structure. This research contributes to the acceleration measurement field of CMUT and can effectively contribute to the breakthrough of vibration acceleration monitoring technology in aerospace, medical equipment, and automotive electronics.

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP).

METHODS

A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150).

RESULTS

In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75).

CONCLUSIONS

The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use.

Associations of Physical Activity and Sedentary Behaviors with Depressive Symptoms and Mood Disturbance Throughout Pregnancy

Purpose: Mental health is critical for a healthy pregnancy, yet few studies have evaluated its associations with best practice, objectively measured moderate- to vigorous-intensity physical activity (MVPA) or sedentary behavior (SED). This study evaluated associations of MVPA and SED with mental health across pregnancy. Materials and Methods: Two cohort studies (total n = 125, mean [standard deviation] 31 [5] years, and 14.4% Black) measured MVPA (waist-worn ActiGraph GT3X) and SED (thigh-mounted activPAL) as well as self-reported depressive symptoms and mood disturbance in each trimester. Associations of group-based trajectories of MVPA and SED with depressive symptoms and mood disturbance were analyzed using regression analyses, both overall and by trimester. Results: Overall, the medium versus low trajectory of MVPA was associated with lower levels of depressive symptoms (B = -1.82, 95% confidence interval [CI] -2.97 to -0.68). In the second trimester, women in either the medium or high MVPA trajectories had lower levels of depressive symptoms compared with women in the low MVPA trajectory (B = -8.73, 95% CI -15.74 to -1.71; and B = -2.18, 95% CI -3.80 to -0.56). SED trajectories were not associated with depressive symptoms. Higher trajectories of MVPA and lower trajectories of SED were associated with lower total mood disturbance, with significant associations in the second trimester for MVPA and the first and second trimesters for SED. Higher MVPA trajectories were associated with higher tension, fatigue, and confusion subscales, while higher SED trajectories were associated with higher anger and fatigue and lower esteem and vigor subscales. Conclusions: MVPA and SED levels appear to affect mental health during pregnancy, although larger prospective studies are warranted. Clinical Trail Registration Number: NCT03084302.

Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app's setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.

Physical Activity Levels During School Recess in a Nationally Representative Sample of 10- to 11-Year-Olds

PURPOSE

School recess provides a valuable opportunity for children's daily moderate- to vigorous-intensity physical activity (MVPA). This study aimed to quantify MVPA during school recess in a representative sample of Scottish children and examine whether recess MVPA varied by gender, socioeconomic status, season, urban/rural residency, and recess length.

METHOD

Five-day accelerometry MVPA data were analyzed from 773 children (53.9% girls, 46.1% boys, 10- to 11-y-olds) from 471 schools. Binary logistic regression explored associations between meeting/not meeting the recommendation to spend 40% of recess time in MVPA and the aforementioned risk factors. Descriptive recess data were also analyzed.

RESULTS

Participants spent an average of 3.2 minutes (SD 2.1) in MVPA during recess. Girls engaged in 2.5 minutes (SD 1.7) of MVPA compared with 4.0 minutes (SD 2.2) for boys. Only 6% of children met the recess MVPA recommendation. The odds of girls (odds ratio 0.09; 95% confidence interval, 0.04-0.25) meeting the recommendation was lower (P < .001) compared with boys. No statistically significant differences were observed in meeting the recommendation for the other risk factors.

CONCLUSION

Levels of MVPA during school recess are very low in Scottish children, and interventions aimed at increasing MVPA during recess are needed.

Optimizing Wearable Device and Testing Parameters to Monitor Running-Stride Long-Range Correlations for Fatigue Management in Field Settings

PURPOSE

There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables.

METHODS

Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100-2000 Hz) and signal length (100-300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable.

RESULTS

Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: -.02 to -.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: -.05 to -.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively).

CONCLUSIONS

Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

Characterization of Sensitivity of Time Domain MEMS Accelerometer

This paper characterizes the sensitivity of a time domain MEMS accelerometer. The sensitivity is defined by the increment in the measured time interval per gravitational acceleration. Two sensitivities exist, and they can be enhanced by decreasing the amplitude and frequency. The sensitivity with minor nonlinearity is chosen to evaluate the time domain sensor. The experimental results of the developed accelerometer demonstrate that the sensitivities span from -68.91 μs/g to -124.96 μs/g and the 1σ noises span from 8.59 mg to 6.2 mg (amplitude of 626 nm: -68.91 μs/g and 10.21 mg; amplitude of 455 nm: -94.51 μs/g and 7.76 mg; amplitude of 342 nm: -124.96 μs/g and 6.23 mg), which indicates the bigger the amplitude, the smaller the sensitivity and the bigger the 1σ noise. The adjustable sensitivity provides a theoretical foundation for range self-adaption, and all the results can be extended to other time domain inertial sensors, e.g., a gyroscope or an inclinometer.

Feasibility of Using a GENEActiv Accelerometer with Triaxial Acceleration and Temperature Sensors to Monitor Adherence to Shoulder Sling Wear Following Surgery

BACKGROUND

Self-reported adherence to sling wear is unreliable due to recall bias. We aim to assess the feasibility and accuracy of quantifying sling wear and non-wear utilising slings pre-fitted with a GENEActiv accelerometer that houses triaxial acceleration and temperature sensors.

METHODS

Ten participants were asked to wear slings for 480 min (8 h) incorporating 180 min of non-wear time in durations varying from 5-120 min. GENEActiv devices were fitted in sutured inner sling pockets and participants logged sling donning and doffing times. An algorithm based on variability in acceleration in three axes and temperature change was developed to identify sling wear and non-wear and compared to participants' logs.

RESULTS

There was no significant difference between algorithm detected non-wear duration (mean ± standard deviation = 172.0 ± 6.8 min/participant) and actual non-wear (179.7 ± 1.0 min/participant). Minute-by-minute agreement of sensor-detected wear and non-wear with participant reported wear was 97.3 ± 1.5% (range = 93.9-99.0), with mean sensitivity 94.3 ± 3.5% (range = 86.1-98.3) and specificity 99.1 ± 0.8% (range = 93.7-100).

CONCLUSION

An algorithm based on accelerometer-assessed acceleration and temperature can accurately identify shoulder sling wear/non-wear times. This method may have potential for assessing whether sling wear adherence after shoulder surgeries have any bearing on patient functional outcomes.

Temporal Interactions between Maintenance of Cerebral Cortex Thickness and Physical Activity from an Individual Person Micro-Longitudinal Perspective and Implications for Precision Medicine

Maintenance of brain structure is essential for neurocognitive health. Precision medicine has interests in understanding how maintenance of an individual person's brain, including cerebral cortical structure, interacts with lifestyle factors like physical activity. Cortical structure, including cortical thickness, has recognized relationships with physical activity, but concepts of these relationships come from group, not individual, focused findings. Whether or how group-focused concepts apply to an individual person is fundamental to precision medicine interests but remains unclear. This issue was studied in a healthy man using concurrent micro-longitudinal tracking of magnetic resonance imaging-defined cortical thickness and accelerometer-defined steps/day over six months. These data permitted detailed examination of temporal relationships between thickness maintenance and physical activity at an individual level. Regression analyses revealed graded significant and trend-level temporal interactions between preceding activity vs. subsequent thickness maintenance and between preceding thickness maintenance vs. subsequent activity. Interactions were bidirectional, delayed/prolonged over days/weeks, positive, bilateral, directionally asymmetric, and limited in strength. These novel individual-focused findings in some ways are predicted, but in other ways remain unaddressed or undetected, by group-focused work. We suggest that individual-focused concepts of temporal interactions between maintenance of cortical structure and activity can provide needed new insight for personalized tailoring of physical activity, cortical, and neurocognitive health.

A GRU-Based Model for Detecting Common Accidents of Construction Workers

Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).

Wearable Sensor to Monitor Quality of Upper Limb Task Practice for Stroke Survivors at Home

Many stroke survivors experience persistent upper extremity impairment that limits performance in activities of daily living. Upper limb recovery requires high repetitions of task-specific practice. Stroke survivors are often prescribed task practices at home to supplement rehabilitation therapy. A poor quality of task practices, such as the use of compensatory movement patterns, results in maladaptive neuroplasticity and suboptimal motor recovery. There currently lacks a tool for the remote monitoring of movement quality of stroke survivors' task practices at home. The objective of this study was to evaluate the feasibility of classifying movement quality at home using a wearable IMU. Nineteen stroke survivors wore an IMU sensor on the paretic wrist and performed four functional upper limb tasks in the lab and later at home while videorecording themselves. The lab data served as reference data to classify home movement quality using dynamic time warping. Incorrect and correct movement quality was labeled by a therapist. The home task practice movement quality was classified with an accuracy of 92% and F1 score of 0.95 for all tasks combined. Movement types contributing to misclassification were further investigated. The results support the feasibility of a home movement quality monitoring system to assist with upper limb rehabilitation post stroke.

Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.

Movement Patterns in Older Adults Recovering From Hip Fracture

The aim of this study was to quantify physical activity and sedentary behavior in older adults recovering from hip fracture and to identify groups based on movement patterns. In this cross-sectional cohort study, older adults (≥70 years) were included 3 months after surgery for proximal femoral fracture. Patients received an accelerometer for 7 days. Demographics and outcomes related to physical function, mobility, cognitive functions, quality of life, and hip fracture were assessed. In total, 43 patients with sufficient accelerometer wear time were included. Across all groups, participants engaged in very low levels of physical activity, spending an average of 11 hr/day in prolonged sedentary behavior. Based on the extracted components from a principal component analysis, three groups with substantial differences in levels of physical activity and sedentary behavior could be distinguished.

Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation

The problem of remaining useful life estimation (RULE) of hollow worn railway vehicle wheels in terms of remaining mileage via wheel tread depth estimation using on-board vibration signals from a single accelerometer on the bogie frame is presently investigated. This is achieved based on the introduction of a statistical time series method that employs: (i) advanced data-driven stochastic Functionally Pooled models for the modeling of the vehicle dynamics under different wheel tread depths in a range of interest until a critical limit, as well as tread depth estimation through a proper optimization procedure, and (ii) a wheel tread depth evolution function with respect to the vehicle running mileage that interconnects the estimated hollow wear with the remaining useful mileage. The method's RULE performance is investigated via hundreds of Simpack-based Monte Carlo simulations with an Attiko Metro S.A. vehicle and many hollow worn wheels scenarios which are not used for the method's training. The obtained results indicate the accurate estimation of the wheels tread depth with a mean absolute error of ∼0.07 mm that leads to a corresponding small error of ∼3% with respect to the wheels remaining useful mileage. In addition, the comparison with a recently introduced Multiple Model (MM)-based multi-health state classification method for RULE, demonstrates the better performance of the postulated method that achieves 81.17% True Positive Rate (TPR) which is significantly higher than the 45.44% of the MM method.

A deep learning wearable-based solution for continuous at-home monitoring of upper limb goal-directed movements

Introduction

Monitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time.

Methods

In this study, we used accelerometer data from stroke survivor participants and controls doing activities of daily living to develop an automated deep learning approach to detect GDMs. The model performance for detecting GDM or non-GDM from windowed data achieved an AUC of 0.9, accuracy 0.83, sensitivity 0.81, specificity 0.84 and F1 0.82.

Results

We further validated the utility of detecting GDM by extracting features from GDM periods and using these features to classify whether the measurements are collected from a stroke survivor or a control participant, and to predict the Fugl-Meyer assessment score from stroke survivors.

Discussion

This study presents a promising and reliable tool for monitoring upper limb function in a real-world setting, and assessing biomarkers related to upper limb health in neurological, neuromuscular and muscles disorders.

Transparency as a Means to Analyse the Impact of Inertial Sensors on Users during the Occupational Ergonomic Assessment: A Systematic Review

The literature has yielded promising data over the past decade regarding the use of inertial sensors for the analysis of occupational ergonomics. However, despite their significant advantages (e.g., portability, lightness, low cost, etc.), their widespread implementation in the actual workplace has not yet been realized, possibly due to their discomfort or potential alteration of the worker's behaviour. This systematic review has two main objectives: (i) to synthesize and evaluate studies that have employed inertial sensors in ergonomic analysis based on the RULA method; and (ii) to propose an evaluation system for the transparency of this technology to the user as a potential factor that could influence the behaviour and/or movements of the worker. A search was conducted on the Web of Science and Scopus databases. The studies were summarized and categorized based on the type of industry, objective, type and number of sensors used, body parts analysed, combination (or not) with other technologies, real or controlled environment, and transparency. A total of 17 studies were included in this review. The Xsens MVN system was the most widely used in this review, and the majority of studies were classified with a moderate level of transparency. It is noteworthy, however, that there is a limited and worrisome number of studies conducted in uncontrolled real environments.

Accelerometer-based and self-reported physical activity of children and adolescents from a seasonal perspective

Background

Many children and adolescents in Europe are insufficiently physically active, which makes the advancement of children's physical activity a critical health promotion target. However, there are some environmental factors, such as the amount of daylight, weather conditions, temperature, and precipitation levels, which might influence physical activity behavior. The purpose of this study was to assess accelerometer-based and self-reported daily physical activity of children and adolescents in Luxembourg, during autumn/winter as well as during spring/summer, and to examine if there is a seasonal influence on the physical activity behavior.

Methods

At two measurements, one in autumn/winter and one in spring/summer, physical activity of N = 137 (59.12% females; M = 12.37 years) participating children and adolescents aged 10-18 years was objectively undertaken via an accelerometer (ActiGraph) and subjectively assessed using, among others, one item of the MoMo physical activity questionnaire.

Results

A repeated measures ANOVA revealed a significant seasonal effect on moderate to vigorous physical activity per day [F(1.000, 135.000) = 7.69, p < 0.05, partial η² = 0.054]. More minutes of moderate to vigorous physical activity per day were accrued in spring/summer than in autumn/winter. The mean difference scores between the accelerometer-based and the self-reported physical activity at the two time periods, T1 and T2, correlated significantly (r = 0.31, p < 0.001).

Conclusions

According to these results, children and adolescents are less physically active in autumn/winter than in spring/summer. However, the discrepancy between the accelerometer-based and the self-reported physical activity remains stable over the two measurements. Therefore, schools, sports clubs, and communities should offer special physical activity programs for the colder season.

Sensing the impact of extreme heat on physical activity and sleep

Introduction

This study assesses the person-specific impact of extreme heat on low-income households using wearable sensors. The focus is on the intensive and longitudinal assessment of physical activity and sleep with the rising person-specific ambient temperature.

Methods

This study recruited 30 participants in a low-income and predominantly Black community in Houston, Texas in August and September of 2022. Each participant wore on his/her wrist an accelerometer that recorded person-specific ambient temperature, sedentary behavior, physical activity intensity (low and moderate to vigorous), and sleep efficiency 24 h over 14 days. Mixed effects models were used to analyze associations among physical activity, sleep, and person-specific ambient temperature.

Results

The main findings include increased sedentary time, sleep impairment with the rise of person-level ambient temperature, and the mitigating role of AC.

Conclusions

Extreme heat negatively affects physical activity and sleep. The negative consequences are especially critical for those with limited use of AC in lower-income neighborhoods of color. Staying home with a high indoor temperature during hot days can lead to various adverse health outcomes including accelerated cognitive decline, higher cancer risk, and social isolation.

Analysis of sedentary behaviour levels and patterns in adults with Down syndrome

BACKGROUND

Sedentary behaviour (SB) among adults with Down syndrome (DS) may differ based on personal or environmental factors.

OBJECTIVE

Investigate differences in SB levels and patterns of adults with DS based on sex, age, and residence-type across weekdays and weekends.

METHODS

Thirty-four adults with DS (15 men; 37 ± 12 years) underwent accelerometry-based measurements of sedentary time, bouts, and breaks for 7 days. We evaluated differences with 2 × 2 mixed-model (group-by-day) ANOVA.

RESULTS

Younger (19-36 years) individuals had less sedentary time (p = .042), and shorter (p = .048) and fewer (p = .012) bouts than older (37-60 years) individuals. Group home residents had more bouts on weekends than adults living with parent/guardians (p = .015).

CONCLUSIONS

Adults with DS spent half their waking hours in SB of short bouts. Age and residence may influence SB.

Medial meniscal lesions increase antero-posterior laxity in knees with anterior cruciate ligament injury

PURPOSE

The aim of this study was to quantify the impact of concomitant meniscal lesions on knee laxity using a triaxial accelerometer in a large population of patients affected by anterior cruciate ligament (ACL) injury.

METHODS

A total of 326 consecutive patients (261 men and 65 women, mean age 31.3 ± 11.3) undergoing primary ACL reconstruction, were preoperatively evaluated through Lachman and pivot shift tests using a triaxial accelerometer to quantify knee laxity. An analysis based on the presence of meniscal tears assessed during surgery was performed to evaluate the impact of meniscal lesions on knee laxity.

RESULTS

The anterior tibial translation (Lachman test) presented significantly higher values in patients with medial meniscal lesions (7.3 ± 1.7 mm, p = 0.049) and both medial and lateral meniscal lesions (7.7 ± 1.6 mm, p = 0.001) compared to patients without concomitant meniscal lesions (6.7 ± 1.3 mm). Moreover, patients with both medial and lateral meniscal lesions presented significantly higher values of anterior tibial translation compared to patients with lateral meniscal lesions (p = 0.049). No statistically significant differences were found between the groups in terms of tibial acceleration (pivot shift test).

CONCLUSION

This study demonstrated that the contribution of concomitant meniscal lesions to knee laxity can be objectively quantified using a triaxial accelerometer in ACL-injured knees. In particular, medial meniscus lesions, alone or in association with lateral meniscus lesions, determine a significant increase of the anterior tibial translation compared to knees without meniscus tears.

LEVEL OF EVIDENCE

Level IV.

Fundament for a methodological standard to process hip accelerometer data to a measure of physical activity intensity in middle-aged individuals

BACKGROUND

There is a lack of a methodological standard to process accelerometer data to measures of physical activity, which impairs data quality and comparability. This study investigated the effect of different combinations of settings of multiple processing components, on the measure of physical activity and the association with measures of cardiometabolic health in an unselected population of middle-aged individuals.

METHODS

Free-living hip accelerometer data, aerobic fitness, body mass index, HDL:total cholesterol ratio, blood glucose, and systolic blood pressure were achieved from 4391 participants 50-64 years old included in The Swedish CArdioPulmonary bioImage Study (SCAPIS) baseline measurement (cross-sectional). Lab data were also included for calibration of accelerometers to provide comparable measure of physical activity intensity and time spent in different intensity categories, as well as to enhance understanding. The accelerometer data processing components were hardware recalibration, frequency filtering, number of accelerometer axes, epoch length, wear time criterium, time composition (min/24 h vs. % of wear time). Partial least regression and ordinary least regression were used for the association analyses.

RESULTS

The setting of frequency filter had the strongest effect on the physical activity intensity measure and time distribution in different intensity categories followed by epoch length and number of accelerometer axes. Wear time criterium and recalibration of accelerometer data were less important. The setting of frequency filter and epoch length also showed consistent important effect on the associations with the different measures of cardiometabolic health, while the effect of recalibration, number of accelerometer axes, wear time criterium and expression of time composition was less consistent and less important. There was a large range in explained variance of the measures of cardiometabolic health depending on the combination of processing settings, for example, 12.1%-20.8% for aerobic fitness and 5.8%-14.0% for body mass index.

CONCLUSIONS

There was a large variation in the physical activity intensity measure and the association with different measures of cardiometabolic health depending on the combination of settings of accelerometer data processing components. The results provide a fundament for a standard to process hip accelerometer data to assess the physical activity in middle-aged populations.

The validity of the Push Band 2.0 to determine speed and power during progressively loaded squat jumps

ABSTARCTThe PUSH band 2.0 is a wearable technology used to measure mean and peak velocity and power in strength-based movements. The agreement between the PUSH band 2.0 and the criterion measure (force plates) during progressively loaded squat jumps was assessed. Fifteen participants performed 3 squat jumps at increasing loads. Linear regression and Bland-Altman plots assessed data simultaneously recorded from both devices. Mean velocity and power showed deviation from the identity line and an overestimation of 7.40% and 25%, respectively. Peak velocity and power showed an overestimation of 14% and underestimation of 6%, respectively. The results support the use of Push Band 2.0 to measure velocity during ballistic squat movements. However, errors in power measurement are greater than acceptable to support in-field use. While peak velocity maintains a consistent overestimation bias across various velocities, mean velocity error increases at higher velocities and can only be considered valid at slow velocities.

Association of sociodemographic, anthropometric, and sleep quality factors with accelerometer-measured sitting and physical activity times among Emirati working women during the COVID-19 pandemic: A cross-sectional study

BACKGROUND

Although a significant lack of physical activity has been linked to an increase in obesity among Emirati women, the factors associated with accelerometer-measured sitting and physical activity times in Emirati women remain unclear.

OBJECTIVES

To explore the association of accelerometer-measured sitting and physical activity times with sociodemographic, anthropometric, and sleep quality factors among Emirati working women.

DESIGN

A cross-sectional study.

METHODS

A convenience sample of 163 healthy working Emirati women aged 18-45 years was used. Sedentary and physical activity times were measured using the Fibion accelerometers worn on the thighs for 4-7 days. General demographic information, anthropometric measurements, and self-reported sleep quality (Pittsburgh sleep quality index score) were collected. Only participants who had valid data (i.e. wear time of ⩾600 min (10 h) per day for a minimum of 4-7 days) were evaluated. All values were normalized to a 16-h day to mitigate differences in wear time among the participants.

RESULTS

Overall, 110 Emirati women were included. The mean sitting time per 16-h day was 11.6 ± 1.1 h; mean moderate activity time per day, 40.88 ± 17.99 min; and mean vigorous activity time per day, 2.41 ± 1.21 min. Longer sitting time was associated with high body fat, secondary education, and divorce. Sitting time was reduced in those with good sleep quality. Moderate-to-vigorous physical activity time was increased in women with postgraduate education and was decreased in women with a longer work experience and with comorbidities. The total activity time increased with increasing age and good sleep quality, whereas it decreased with increasing body fat, presence of at least one comorbidity, secondary education, and divorce.

CONCLUSION

Certain demographic, anthropometric, and sleep quality factors were associated with accelerometer-measured sitting and physical activity times among Emirati working women. Future longitudinal studies should consider these factors when investigating predictors of physical activity levels in this population.

Extracorporeal driveline vibrations to detect left ventricular assist device thrombosis - A porcine model study

BACKGROUND

Pump thrombosis (PT) and related adverse complications contributed to the HeartWare Ventricular Assist Device (HVAD) market withdrawal. Many patients still receive lifelong support, with deficient PT surveillance based on pump power trends. Analysis of pump vibrations is better for detecting PT. Here, we investigated the feasibility of an extracorporeal accelerometer to detect PT from pump vibrations propagated out on the driveline.

METHODS

In a porcine HVAD model (n = 6), an accelerometer was attached to the pump as a reference and another to the driveline for comparisons of signals. In total, 59 thrombi were injected into the heart to induce PT, followed by intermittent thrombus washout maneuvers. Signals were compared visually in spectrograms and quantitatively in third harmonic saliences (S3H) by correlation analysis. Receiver operating characteristic curves expressed the method's outcome in sensitivity vs specificity, with the overall diagnostic performance in the area under the curve (AUC) score.

RESULTS

Five experiments had good driveline signal strength, with clear spectrographic relationships between the 2 accelerometers. Third harmonic driveline vibrations were visible 20 vs 30 times in the reference. The comparison in S3H showed a strong correlation and yielded an AUC of 0.85. Notably, S3H proved robust regarding noise and false PT detections.

CONCLUSIONS

An extracorporeal accelerometer on the driveline can be a readily available method for accurate HVAD PT detection before an accelerometer integration with left ventricular assist device is feasible.