Commercially available wearable health monitors in dogs only had a very strong correlation during longer durations of time: a pilot study.

OBJECTIVE: To compare a commercially available accelerometer, FitBark 2 (FitBark 2nd Generation; FitBark) with a previously validated accelerometer, Actical (Actical; Respironics Inc) during periods of activity and rest. We hypothesized that the FitBark 2 would correlate strongly with the Actical during periods of activity and rest. METHODS: 20 dogs between the ages of 1 and 9 years of variable sex, breed, and body weight were enrolled from April through August 2022 in a 1-week pilot trial. Dogs were fitted with a neck collar with both devices mounted on it and wore it continuously for 1 week. Six time points were established to evaluate varying degrees of activity and periods of rest, which included the week, a 1-mile walk, the day of the walk, the hour the walk occurred in, 6 am to 7 am on the morning of the walk, and 11 pm to 12 am on the evening of the walk. Actical and FitBark 2 were compared using linear regression and correlation analysis. RESULTS: Correlation analysis revealed a very strong correlation between the Actical and FitBark 2 during the entire week of the study, with a moderate correlation at other time points. Linear regression revealed the strength of the relationship by the r2 = 0.85, F1,13 = 76.08. CONCLUSIONS: FitBark 2 can be used to evaluate the activity and rest of dogs with varying degrees of correlation when compared to the Actical. CLINICAL RELEVANCE: The time period assessed impacts the correlation of the FitBark 2 and Actical. This should be considered when using the FitBark 2 for data collection.

Comparison of community ambulation intensity determined from overground and treadmill walking cadence cut points: Is there a relationship between step activity measures and predicted VO2 max in healthy children and adults?

BACKGROUND: This study was designed to evaluate the effect of using maximal cadence criteria cut points established during overground and treadmill walking, on intensity outputs measured during community ambulation. The second aim was to establish the relationship between cardiovascular fitness (predicted VO2 max capacity) and community ambulation intensity performance, in typically developing individuals. RESEARCH QUESTION: What is the effect on intensity measures when using cadence cut point criteria derived from overground and treadmill walking and does predicted VO2 max correlate with exercise related community activity in a typically developing population? METHODS: A group of 37 typically developing participants between 8 and 27 years of age, underwent a graded submaximal VO2 testing protocol followed by a typical week of community ambulation, recorded with a step activity monitor. Maximum cadence criteria established during overground and treadmill walking were applied and the data were compared. The weekly step activity variables included: total steps, total ambulatory time, intensity, duration, and volume. Predicted VO2 Max was calculated, and correlations calculated to step activity outputs. RESULTS: Results showed significant differences (p<0.001) between cadence cut point criteria across all intensity measures except in the amount of time spent in the 30-60 % intensity category (p=0.182). Predicted VO2 max did not significantly correlate with step activity outputs related to exercise (moderate+ intensity and long duration ambulatory bouts; p>0.277). SIGNIFICANCE: This study illustrates the importance of close consideration in applying recommended cut off criteria when assessing intensity outputs from step activity data. Cadence from both overground and treadmill walking were collected in a controlled lab setting, but the influence of the treadmill mechanical and forced cadence criteria must be considered when selecting intensity cut points.

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment.

AIM: The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment. METHODS: Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman's correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland-Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI). RESULTS: Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland-Altman graph. CONCLUSION: The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty.

PURPOSE: The aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery. METHODS: Twenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques. RESULTS: Mean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from - 0.87 to 1.36 with limits of agreement (LOA) ranging between - 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between - 2.8 and 5.73 with LOA between - 13.51 and 24.97. CONCLUSIONS: These devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

The use of accelerometers to improve estimation of the thermic effect of food in whole room calorimetry studies.

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement.NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7-22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = -0.15; 95% LOA [-0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

Accelerometer-based measures in Friedreich ataxia: a longitudinal study on real-life activity.

Quantitative measurement of physical activity may complement neurological evaluation and provide valuable information on patients' daily life. We evaluated longitudinal changes of physical activity in patients with Friedreich ataxia (FRDA) using remote monitoring with wearable sensors. We performed an observational study in 26 adult patients with FRDA and 13 age-sex matched healthy controls (CTR). Participants were asked to wear two wearable sensors, at non-dominant wrist and at waist, for 7 days during waking hours. Evaluations were performed at baseline and at 1-year follow-up. We analysed the percentage of time spent in sedentary or physical activities, the Vector Magnitude on the 3 axes (VM3), and average number of steps/min. Study participants were also evaluated with ataxia clinical scales and functional tests for upper limbs dexterity and walking capability. Baseline data showed that patients had an overall reduced level of physical activity as compared to CTR. Accelerometer-based measures were highly correlated with clinical scales and disease duration in FRDA. Significantly changes from baseline to l-year follow-up were observed in patients for the following measures: (i) VM3; (ii) percentage of sedentary and light activity, and (iii) percentage of Moderate-Vigorous Physical Activity (MVPA). Reduction in physical activity corresponded to worsening in gait score of the Scale for Assessment and Rating of Ataxia. Real-life activity monitoring is feasible and well tolerated by patients. Accelerometer-based measures can quantify disease progression in FRDA over 1 year, providing objective information about patient's motor activities and supporting the usefulness of these data as complementary outcome measure in interventional trials.

Differential relationships between physical activity and pain phenotypes in individuals with spinal cord injury.

BACKGROUND: Chronic pain affects 70% of individuals with spinal cord injury (SCI) and leads to declines in health and quality of life. Neuropathic and nociceptive pain are phenotypes derived from different mechanisms that contribute to pain perception. The objective of this research was to investigate differential pain responses to moderate-to-vigorous physical activity (MVPA) in two chronic pain phenotypes: neuropathic and nociceptive pain. METHODS: Community-based physical activity levels were collected for one week in 17 individuals with SCI using a wrist-worn accelerometer, and daily pain ratings were assessed and categorized by phenotype. Physical activity levels were summarized to calculate minutes of MVPA. Correlational analyses were conducted to compare relationships between pain intensity and MVPA across individual participants and between pain phenotype groups. RESULTS: The neuropathic pain group revealed significant negative correlation between MVPA and pain intensity. In the nociceptive pain group, there was no significant correlation between MVPA and pain intensity. Further analysis revealed two subgroups of positive (N = 4) and negative (N = 3) correlations between MVPA and pain intensity. Pain location differed between the subgroups of nociceptive pain. Individuals with negative correlation experienced neck and upper back pain, whereas individuals with positive correlation experienced unilateral upper extremity pain. CONCLUSION: Differential relationships exist between pain phenotypes and MVPA in individuals with SCI. Pain location differed between the subgroups of nociceptive pain, which we presume may indicate the presence of nociplastic pain in some individuals. These results may contribute to the advancement of personalized pain management by targeting non-pharmacological interventions for specific pain phenotypes.Trial registration: ClinicalTrials.gov identifier: NCT05236933..

Randomised Controlled Trial of a Customised Text Messaging and Activity Monitor Program for Lifestyle Improvement after Gestational Diabetes.

Gestational diabetes (GDM) is associated with a long-term risk of diabetes. We aimed to determine whether a text-messaging-based lifestyle support program would improve diabetes risk factors following GDM. Women with GDM were randomised following delivery to receive four text messages per week supporting a healthy lifestyle and parenting for 6 months, with feedback from an activity monitor (intervention), or to receive the activity monitor only (control). The primary outcome was a composite of weight, physical activity and dietary goals. There were 177 women randomised, with 88 intervention and 89 control participants. All the participants experienced COVID-19 lockdowns during the study. Six-month primary outcome data were obtained for 57 intervention participants and 56 controls. There were 7/57 (12%) intervention and 6/56 (11%) control participants who met the primary outcome (relative risk, 1.08; 95%CI, 0.63-1.85; p = 0.79). Two intervention participants met the dietary goals compared to none of the control participants (p = NS). The intervention participants were more likely to record >1000 steps/day (on 102 ± 59 vs. 81 ± 59 days, p = 0.03). When analysed monthly, this was not initially different but became significant 3-6 months post-partum. Interviews and surveys indicated that with the Intervention, healthier choices were made, but these were negatively impacted by COVID-19 restrictions. Participants found the messages motivational (74%) and the activity monitor useful (71%). In conclusion, no improvement in the diabetes risk factors occurred among the women receiving the text messaging intervention when affected by COVID-19 restrictions.

Examining a Remote Group-Based Type 2 Diabetes Self-Management Education Program in the COVID-19 Era Using the ORBIT Model: Small 6-Week Feasibility Study.

BACKGROUND: To date, most group-based diabetes self-management education (DSME) programs for type 2 diabetes (T2D) have been delivered in person. The rapid transition to remote care at the outset of the COVID-19 pandemic presented opportunities to test, evaluate, and iterate a new remote DSME program. OBJECTIVE: We aim to refine the delivery and evaluation of a multicomponent remote DSME program for adults living with T2D by examining several feasibility outcomes. METHODS: We recruited a convenience sample of patients from a London, Canada, outpatient diabetes clinic (serving high-risk, low-income adults) to participate in a 6-week, single cohort feasibility study from November 2020 to March 2021. This small ORBIT phase 1b feasibility study represents the first in a planned series guided by the ORBIT model for developing behavioral interventions for chronic diseases (phase 1: design; phase 2: preliminary testing; phase 3: efficacy; and phase 4: effectiveness). The feasibility of delivering and evaluating a remote DSME program, including (1) live video education classes, (2) individualized physical activity (PA) prescription and counseling, and (3) intermittently scanned continuous glucose and wearable PA monitoring, was assessed. Feasibility outcomes included recruitment and retention rates, program adherence, and acceptability (ie, technology issues and exit survey feedback). PA was assessed with Fitbit Inspire 2 (Fitbit Inc) and estimated glycated hemoglobin (HbA1c) using the FreeStyle Libre (Abbot). Given the small study sample, group- and individual-level data are reported descriptively. RESULTS: A total of 10 adults living with T2D were recruited (female 60%; age 49.9, SD 14.3 years; estimated HbA1c 6.2%, SD 0.5%). Recruitment and retention rates were 29% and 80%, respectively. Participants attended 83% (25/30) and 93% (37/40) of education classes and PA counseling phone calls, respectively. There were 3.2 (SD 2.6) technology issues reported per person, most of which were related to study data transfer. Exit survey responses suggest most participants (8/9, 89%) were "satisfied" with the program. Recognizing the small sample size and the fact that no inferential statistics were conducted, the mean (SD) for the weekly daily step count and estimated HbA1c are provided for illustrative purposes. Participants accumulated 7103 (SD 2900) and 7515 (SD 3169) steps per day at baseline and week 6, respectively. The estimated HbA1c was 6.2% (SD 0.5%) and 6.2% (SD 0.6%) at baseline and week 6, respectively. CONCLUSIONS: This ORBIT phase 1b study served to refine the delivery (eg, automatic study data upload process recommended to reduce participant burden) and evaluation (eg, purposeful sampling of participants with baseline HbA1c >8% recommended to address selection bias) of a remote DSME program. Preliminary proof-of-concept testing (ORBIT phase 2) incorporating some of these learnings is now warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT04498819; https://clinicaltrials.gov/study/NCT04498819.

Validation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease.

BACKGROUND: Consumer and research activity monitors have become popular because of their ability to quantify energy expenditure (EE) in free-living conditions. However, the accuracy of activity trackers in determining EE in people with Huntington's Disease (HD) is unknown. RESEARCH QUESTION: Can the ActiGraph wGT3X-B or the Fitbit Charge 4 accurately measure energy expenditure during physical activity, in people with HD compared to Indirect Calorimetry (IC) (Medisoft Ergo Card)? METHODS: We conducted a cross-sectional, observational study with fourteen participants with mild-moderate HD (mean age 55.7 ± 11.4 years). All participants wore an ActiGraph and Fitbit during an incremental test, running on a treadmill at 3.2 km/h and 5.2 km/h for three minutes at each speed. We analysed and compared the accuracy of EE estimates obtained by Fitbit and ActiGraph against the EE estimates obtained by a metabolic cart, using with Intra-class correlation (ICC), Bland-Altman analysis and correlation tests. RESULTS: A significant correlation and a moderate reliability was found between ActiGraph and IC for the incremental test (r = 0.667)(ICC=0.633). There was a significant correlation between Fitbit and IC during the incremental test (r = 0.701), but the reliability was poor at all tested speeds in the treadmill walk. Fitbit significantly overestimated EE, and ActiGraph underestimated EE compared to IC, but ActiGraph estimates were more accurate than Fitbit in all tests. SIGNIFICANCE: Compared to IC, Fitbit Charge 4 and ActiGraph wGT3X-BT have reduced accuracy in estimating EE at slower walking speeds. These findings highlight the need for population-specific algorithms and validation of activity trackers.

International Physical Activity Questionnaire Short Form and accelerometer-assessed physical activity: concurrent validity using six cut-points in HF patients.

AIMS: Physical activity (PA) is an important target for improving clinical outcomes in heart failure (HF) patients. Nonetheless, assessing the daily PA profile in this population is a challenging task, traditionally performed using self-report questionnaires such as the International PA Questionnaire Short Form (IPAQ-SF). This study aimed to evaluate the concurrent validity of the IPAQ-SF and accelerometer-assessed PA using six published cut-points in patients with HF and reduced or mildly reduced ejection fraction. METHODS AND RESULTS: The concordance between the IPAQ-SF and a hip-worn accelerometer regarding daily time spent performing moderate to vigorous PA in bouts of at least 10 min was assessed in 53 participants for seven consecutive days using six different cut-points (Barnett, Dibben, Mark, Sanders, Troiano, and Vaha-Ypya). Spearman's correlation and Bland-Altman plots were used to evaluate concurrent validity between methods. Regressions were used to study the association between patient variables, wear protocol (waking hour or 24 h), and absolute bias. The kappa index was used to evaluate the concordance between IPAQ-SF and accelerometry for classifying patients as active or non-active. All analyses were re-run using non-bouted metrics to investigate the effect of bouted versus non-bouted analysis. The IPAQ-SF and accelerometry showed low to negligible correlation (ρ = 0.12 to 0.37), depending on the cut-point used. The regression analysis showed that the absolute bias was higher in participants following the waking-hour protocol at all cut-points except Dibben's (P ≤ 0.007). The concordance between the two methods to classify patients as active and non-active was low when using Mark (κ = 0.23) and Barnett (κ = 0.34) cut-points and poor for the remaining cut-points (κ = 0.03 to 0.18). The results of the sensitivity analysis showed negligible to low correlation using non-bouted metrics (ρ = 0.27 to 0.33). CONCLUSIONS: Moderate to vigorous PA measures using IPAQ-SF and accelerometers are not equivalent, and we do not encourage researchers to use IPAQ-SF alone when assessing PA in HF patients. Moreover, applying personalized collection and processing criteria is important when assessing PA in HF patients. We recommend following the 24 h protocol and selecting cut-points calibrated in patients with cardiovascular diseases. Finally, it is necessary to develop a new tailored questionnaire that considers walking intensity and is adjusted to the current World Health Organisation recommendations, which use non-bouted metrics.

Wearable activity data can predict functional recovery after musculoskeletal injury: Feasibility of a machine learning approach.

Delayed functional recovery after injury is associated with significant personal and socioeconomic burden. Identification of patients at risk for a prolonged recovery after a musculoskeletal injury is thus of high relevance. The aim of the current study was to show the feasibility of using a machine learning assisted model to predict functional recovery based on the pre- and immediate post injury patient activity as measured with wearable systems in trauma patients. Patients with a pre-existing wearable (smartphone and/or body-worn sensor), data availability of at least 7 days prior to their injury, and any musculoskeletal injury of the upper or lower extremity were included in this study. Patient age, sex, injured extremity, time off work and step count as activity data were recorded continuously both pre- and post-injury. Descriptive statistics were performed and a logistic regression machine learning model was used to predict the patient's functional recovery status after 6 weeks based on their pre- and post-injury activity characteristics. Overall 38 patients (7 upper extremity, 24 lower extremity, 5 pelvis, 2 combined) were included in this proof-of-concept study. The average follow-up with available wearable data was 85.4 days. Based on the activity data, a predictive model was constructed to determine the likelihood of having a recovery of at least 50 % of the pre-injury activity state by post injury week 6. Based on the individual activity by week 3 a predictive accuracy of over 80 % was achieved on an independent test set (F1=0,82; AUC=0,86; ACC=8,83). The employed model is feasible to assess the principal risk for a slower recovery based on readily available personal wearable activity data. The model has the potential to identify patients requiring additional aftercare attention early during the treatment course, thus optimizing return to the pre-injury status through focused interventions. Additional patient data is needed to adapt the model to more specifically focus on different fracture entities and patient groups.

Agreement of activity monitors for assessment of patients with sub-acute stroke in an inpatient rehabilitation facility.

PURPOSE: Determine the level of agreement of three activity monitors compared with the gold standard (video review) on the activity level of patients with stroke. METHODS: A prospective, observational, agreement study was performed on 47 individuals with sub-acute stroke in an inpatient rehabilitation facility. Data was collected during one physical therapy session. Individuals wore three device types; Actigraph (AG), Activpal (AP), and stepwatch activity monitor (SAM). Variables assessed were step counts for each limb (hemiparetic and non-hemiparetic) and percent time standing and other. ANALYSIS: Results from the activity monitors were compared to the video review and assessed for agreement using the intraclass correlation coefficient (ICC) and accuracy of mean difference from video observation. RESULTS: The step counts with the SAM on the non-hemiparetic limb had the highest ICC for step counts (ICC = 0.98, p < 0.001) and were overestimated with 21% accuracy. The SAM on the hemiparetic limb had 9.7% accuracy (ICC = 0.92, p < 0.001). For percent standing time all devices overestimated with poor reliability. For percent other activity time, the AP had the best accuracy and underestimated for both the hemiparetic limb (9.9% accuracy; ICC = 0.90, p < 0.001) and non-hemiparetic limb (8.3% accuracy; ICC = 0.84, p < 0.001). CONCLUSIONS: The use of multiple devices may be warranted to capture an accurate understanding of activity levels in this population of individuals with sub-acute stroke. There are concerns with all monitors and clinicians and researchers should be aware of what measures they are wanting to understand about their population.

The stepwatch activity monitor worn on the hemiparetic limb provided the best accuracy and excellent reliability for step counts in this population of subacute stroke.For percent standing time all devices overestimated with poor reliability.For percent other time, the AP had the best accuracy and good reliability on the non-hemiparetic limb.The use of multiple devices may be warranted to capture a more accurate understanding of activity level in this population of individuals with sub-acute stroke.Clinicians and researchers need to be aware of the biases of these devices in this population.

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson's Disease through a Commercial Smartwatch.

Daily steps could be a valuable indicator of real-world ambulation in Parkinson's disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland-Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2-5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann-Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84-0.90; SAM range: 7.8-9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD.

Technical Assistance Received by Older Adults to Use Commercially Available Physical Activity Monitors (Ready Steady 3.0 Trial): Ad-Hoc Descriptive Longitudinal Study.

Background: Despite evidence that regular physical activity (PA) among older adults confers numerous health and functional benefits, PA participation rates are low. Using commercially available wearable PA monitors (PAMs) is one way to augment PA promotion efforts. However, while expert recommendations exist for the specific information needed at the beginning of PAM ownership and the general ongoing need for structures that support as-needed technical troubleshooting, information is lacking about the type, frequency, and modes of assistance needed during initial and long-term ownership. Objective: This paper describes problems reported and technical assistance received by older adults who used PAMs during the 18 months they participated in a community-based PA trial: Ready Steady 3.0 (RS3). Methods: This was an ad-hoc longitudinal analysis of process variables representing technical problems reported and assistance received by 113 RS3 study participants in the 18 months after their orientation to PAMs. Variables included date of contact, problem(s) reported, mode of technical assistance, and whether the equipment was replaced. The descriptive analysis included frequencies and incidence rates of distinct contacts, types of problems, and technical assistance modes. Results: On average, participants were aged 77 (SD 5.2) years. Most identified as female (n=87, 77%), reported experience using smartphones (n=92, 81.4%), and used the PAM between 2 and 18 months. Eighty-two participants (72.6%) reported between 1 to 9 problems with using PAMs, resulting in a total of 150 technical assistance contacts with a mean of 1.3 (SD 1.3) contacts. The incidence rate of new, distinct contacts for technical assistance was 99 per 100 persons per year from 2018 to 2021. The most common problems were wearing the PAM (n=43, 28.7%), reading its display (n=23, 15.3%), logging into its app (n=20, 13.3%), charging it (n=18, 12%), and synchronizing it to the app (n=16, 10.7%). The modalities of technical assistance were in person (n=53, 35.3%), by telephone (n=51, 34%), by email (n=25, 16.7%), and by postal mail (n=21, 14%). Conclusions: In general, the results of this study show that after receiving orientation to PAMs, problems such as uncomfortable wristbands, difficulty using the PAM or its related app, and obtaining or interpreting relevant personal data were occasionally reported by participants in RS3. Trained staff helped participants troubleshoot and solve these technical problems primarily in person or by phone. Results also underscore the importance of involving older adults in the design, usability testing, and supportive material development processes to prevent technical problems for the initial and ongoing use of PAMs. Clinicians and researchers should further assess technical assistance needed by older adults, accounting for variations in PAM models and wear time, while investigating additional assistance strategies, such as proactive support, short GIF videos, and video calls.

Timing of Artificial Insemination Using Sexed or Conventional Semen Based on Automated Activity Monitoring of Estrus in Holstein Heifers.

Investigations on the optimum timing of artificial insemination (AI) following automated activity monitoring (AAM) depending on different types of semen in heifers are limited and in part show controversial results. Therefore, the objective of this observational study was to determine the association between the timing of AI using different characteristics of estrus (i.e., the onset, peak, and end of estrus) and pregnancy per AI (P/AI) in Holstein heifers. Heifers were fitted with a neck-mounted AAM system and inseminated with frozen conventional and sexed semen. The pregnancy per AI (n = 4159) from 2858 heifers from six commercial dairy farms in Germany inseminated upon the alert of an AAM system was evaluated. Estrous intensity was classified based on peak activity into low (35 to 89 index value) and high (90 to 100 index value). We detected a quadratic association between the interval from the onset of estrus to AI and P/AI (p = 0.02). The greatest P/AI was observed for heifers inseminated from 9 to 32 h after the onset of estrus. The intervals from the peak of activity to AI and the end of estrus to AI were not associated with P/AI (p ≥ 0.05). Heifers inseminated with frozen conventional semen (50.1%) had a greater P/AI compared with heifers inseminated with frozen sexed semen (43.3%; p = 0.03). There were no interactions between the intervals from the onset, peak, or end of estrus to AI or the type of semen and the P/AI (p ≥ 0.05). The pregnancy per AI was not associated with estrous intensity (50.5% for low intensity vs. 53.0% for high intensity; p = 0.37). In conclusion, inseminating heifers between 9 and 32 h after the onset of estrus, as detected by the AAM, optimized the P/AI regardless of semen type.

Potential limitations of measuring ambulatory activity of part-time wheelchair users: a comparative study of two research grade activity monitors.

Objective.Research grade activity monitors such as the StepWatch Activity Monitor (SAM) and Actigraph have been shown to be highly accurate for the assessment of ambulatory activity, but some individuals function in the community using a combination of both walking and wheelchair activity. The purpose of this study was to assess the accuracy of the SAM and Actigraph at not detecting ambulatory activity, during wheelchair activities at two intensity levels.Approach.Two independent cohorts were tested while simultaneously wearing a SAM and Actigraph on the ankle. Group 1 consisted of twelve adults performing a six-minute push test (6MPT). Group 2 were eighteen members of an adolescent wheelchair basketball team tested during a scrimmage/practice and regional tournament. Visual observation was used to confirm the number of ambulatory steps taken during testing was zero.Main results.The average number of 'steps' detected during a 6MPT was 19 ± 25 (range: 0-85) with the Actigraph. No 'steps' were detected using the SAM in Group 1. In Group 2, the average 'step' count during basketball practice was 1421 ± 750 (range: 612-2798) using the Actigraph and 1363 ± 1238 (range: 84-3462) using the SAM, and correlated with playing time, despite the leg/ankle being strapped down. Similar findings were observed during the wheelchair basketball regional tournament.Significance.Caution should be heeded when utilizing these activity monitors in individuals with disabilities who participate in wheelchair sports. Additional written activity logs should be used to exclude periods of time during which high acceleration wheelchair sporting activity occurs, or study participants should be fully educated in removing the activity monitors during non-ambulatory wheelchair sports.

Day-to-day variability in accelerometer-measured physical activity in mid-aged Australian adults.

PURPOSE: The aim was to use accelerometer data to describe day-to-day variability in physical activity in a single week, according to sociodemographic variables, in mid-aged Australian adults. METHODS: Data were from participants in the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study who took part in a 2014 sub-study (N = 612; Mean age 60.6 [SD 6.9; range 48-73]). Participants wore a triaxial accelerometer (ActiGraph wGT3X-BT) on their non-dominant wrist for seven days, and data were expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g). These were, used to estimate daily acceleration (during waking hours) and daily time spent in moderate-vigorous physical activity (MVPA, defined as ≥ 100mg). Coefficient of variation (calculated as [standard deviation/mean of acceleration and MVPA across the seven measurement days] * 100%) was used to describe day-to-day variability. RESULTS: Average values for both acceleration (24.1-24.8 mg/day) and MVPA (75.9-79.7 mins/day) were consistent across days of the week, suggesting little day-to-day variability (at the group level). However, over seven days, average individual day-to-day variability in acceleration was 18.8% (SD 9.3%; range 3.4-87.7%) and in MVPA was 35.4% (SD 15.6%; range 7.3-124.6%), indicating considerable day-to-day variability in some participants. While blue collar workers had the highest average acceleration (28.6 mg/day) and MVPA (102.5 mins/day), their day-to-day variability was low (18.3% for acceleration and 31.9% for MVPA). In contrast, variability in acceleration was highest in men, those in professional occupations and those with high income; and variability in MVPA was higher in men than in women. CONCLUSION: Results show group-level estimates of average acceleration and MVPA in a single week conceal considerable day-to-day variation in how mid-age Australians accumulate their acceleration and MVPA on a daily basis. Overall, there was no clear relationship between overall volume of activity and variability. Future studies with larger sample sizes and longitudinal data are needed to build on the findings from this study and increase the generalisability of these findings to other population groups.

A feasibility study to evaluate a purposeful walk intervention with a distance goal using a commercially available activity monitor in elderly people post total hip replacement surgery.

Introduction: Total hip replacement (THR) is performed in an increasing number of individuals around the world and while improvements in pain reduction and long-term enhancement of muscle strength are well documented, the improvement in daily activity does not follow the same trend. This study aimed to determine the feasibility of a 5-week intervention where a personalised outdoor walking distance is monitored using a commercial activity monitor (Fitbit Charge 4). Method: Data was collected on gait and activities of daily living using patient reported outcome measures. Following the completion of the intervention period, participants took part in a semi-structured interview to voice their opinion on the use of the activity monitor, their experiences, and any challenges in order to assess the feasibility of the intervention. All quantitative data were presented descriptively, using appropriate summary statistics. Interviews were analysed using thematic analysis. Results: Five participants who had undergone total hip replacement surgery within the postoperative period of 3 to 6 months were recruited from the local community. Conclusion: The findings suggest that the intervention was feasible and that it encouraged all participants to increase their daily activity. Therefore, it can be concluded that a follow-up effectiveness trial is warranted.