Non-consent to a wrist-worn accelerometer in older adults: the role of socio-demographic, behavioural and health factors.
Ontology highlight
ABSTRACT:
SUBMITTER: Hassani M
PROVIDER: S-EPMC4208789 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Non-consent to a wrist-worn accelerometer in older adults: the role of socio-demographic, behavioural and health factors.
Hassani Maliheh M Kivimaki Mika M Elbaz Alexis A Shipley Martin M Singh-Manoux Archana A Sabia Séverine S
PloS one 20141024 10
<h4>Background</h4>Accelerometers, initially waist-worn but increasingly wrist-worn, are used to assess physical activity free from reporting-bias. However, its acceptability by study participants is unclear. Our objective is to assess factors associated with non-consent to a wrist-mounted accelerometer in older adults.<h4>Methods</h4>Data are from 4880 Whitehall II study participants (1328 women, age range = 60-83), requested to wear a wrist-worn accelerometer 24 h every day for 9 days in 2012/ ...[more]
Similar Datasets
Project description:Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
| S-EPMC7794504 | biostudies-literature
Project description:Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants' performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was "carried" by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for "carried" were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the "carried" behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and "carried" as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being "carried" from ambulatory movements.
| S-EPMC6678133 | biostudies-literature
Project description:BackgroundAgreement between questionnaires and accelerometers to measure physical activity (PA) differs between studies and might be related to demographic, lifestyle, and health characteristics, including disability and depressive symptoms.MethodsWe included 1,410 individuals aged 51-94 years from the population-based Rotterdam Study. Participants completed the LASA Physical Activity Questionnaire and wore a wrist-worn accelerometer on the nondominant wrist for 1 week thereafter. We compared the Spearman correlation and disagreement (level and direction) for total PA across levels of demographic, lifestyle, and health variables. The level of disagreement was defined as the absolute difference between questionnaire- and accelerometer-derived PA, whereas the direction of disagreement was defined as questionnaire PA minus accelerometer PA. We used linear regression analyses with the level and direction of disagreement as outcome, including all demographic, lifestyle, and health variables in the model.ResultsWe observed a Spearman correlation of 0.30 between questionnaire- and accelerometer-derived PA in the total population. The level of disagreement (ie, absolute difference) was 941.9 (standard deviation [SD] 747.0) minutes/week, and the PA reported by questionnaire was on average 529.4 (SD 1,079.5) minutes/week lower than PA obtained by the accelerometer. The level of disagreement decreased with higher educational levels. Additionally, participants with obesity, higher disability scores, and more depressive symptoms underestimated their self-reported PA more than their healthier counterparts.ConclusionWe observed large differences in PA time derived from the LASA Physical Activity Questionnaire and the wrist-worn accelerometer. Differences between the methods were related to body-mass index, level of disability, and presence of depressive symptoms. Future studies using questionnaires and/or accelerometers should account for these differences.
| S-EPMC5739112 | biostudies-literature
Project description:BackgroundHip- and wrist-worn ActiGraph accelerometers are widely used in research on physical activity as they offer an objective assessment of movement intensity across the day. Herein we characterize and contrast key structured physical activities and common activities of daily living via accelerometry data collected at the hip and wrist from a sample of community-dwelling older adults.MethodsLow-active, older adults with obesity (age 60+ years) were fit with an ActiGraph GT3X+ accelerometer on their nondominant wrist and hip before completing a series of tasks in a randomized order, including sitting/standing, sweeping, folding laundry, stair climbing, ambulation at different intensities, and cycling at different intensities. Participants returned a week later and completed the tasks once again. Vector magnitude counts/second were time-matched during each task and then summarized into counts/minute (CPM).ResultsMonitors at both wear locations similarly characterized standing, sitting, and ambulatory tasks. A key finding was that light home chores (sweeping, folding laundry) produced higher and more variable CPM values than fast walking via wrist ActiGraph. Regression analyses revealed wrist CPM values were poor predictors of hip CPM values, with devices aligning best during fast walking (R2 = 0.25) and stair climbing (R2 = 0.35).ConclusionsAs older adults spend a considerable portion of their day in nonexercise activities of daily living, researchers should be cautious in the use of simply acceleration thresholds for scoring wrist-worn accelerometer data. Methods for better classifying wrist-worn activity monitor data in older adults are needed.
| S-EPMC9923693 | biostudies-literature
Project description:BackgroundWidespread use of antibiotics has led to the development of antibiotic resistance. However, there are limited data describing antibiotic use in the community setting, and examining factors associated with greater use. Our study aimed to quantify antibiotic dispensing in older adults in the community according to socio-demographics and health services use.MethodsProspective analysis of a population-based cohort study of 239,981 adults aged ?45 years in Australia (the Sax Institute's 45 and Up Study). Data on socio-demographics and health from a questionnaire, were linked to 2015 antibiotic dispensing data from the Pharmaceutical Benefits Scheme (PBS), as well as other administrative health databases. We estimated the Defined Daily Dose (DDD) of systemic antibiotics dispensed, defined by an Anatomic Therapeutic Classification code beginning with J01, in 2015. We also conducted Poisson regression with robust standard errors to identify factors associated with antibiotic dispensing.ResultsOverall, 49.3% of 45 and Up Study participants had at least one systemic antibiotic dispensed in 2015 with a total of 392,856 prescriptions dispensed and an average of 36.5 DDDs/1000-persons/day in the study population. The quantity of antibiotics dispensed increased with increasing age (25.6 DDDs/1000/day in <60 years old versus 50.4 DDDs/1000/day in 80+ year old) and was higher comparing women to men (39.9 versus 32.4 DDDs/1000/day). Of factors examined, the greatest dispensing of antibiotics was among those who had been resident in an aged care facility and those with >15 general practitioner consultations in the last year (80.5 and 88.3 DDDs/1000/day, respectively). These factors remained strongly associated with greater antibiotic dispensing after adjusting for age, sex, education, income, area of residence and co-morbidities.ConclusionsResidence in aged care facilities and high GP visits are associated with greater antibiotic dispensing. This study provides important evidence regarding high use groups for antimicrobial stewardship.
| S-EPMC6715220 | biostudies-literature
Project description:BackgroundSince the traditional models missed the possibility of formulating personalised programs centred on a person's priorities and values, it was a pressing priority to shift from traditional disease-centred to a function-based approach of healthy ageing, which is defined as 'the process of developing and maintaining the functional ability that enables well-being in older age'. The present study aimed to assess the prevalence of high intrinsic capacity (IC) of older adults and to examine the socio-demographic and lifestyle factors associated with IC among older adults in India.MethodsThe study utilises the individual-level data from the first wave of the Longitudinal Aging Study in India (LASI) conducted during 2017-18. The total sample size for the present study was 24,136 older adults (11,871 males and 12,265 females) aged 60 years and above. Descriptive statistics, along with bivariate analysis, was employed to present the preliminary results. Additionally, multivariable linear and logistic regression analyses were conducted to find out the association of socio-demographic and lifestyle factors with IC and its components.ResultsThe mean IC score was found to be 7.37 (SD = 1.6) in this study. A proportion of 24.56% of older adults was observed to be in the higher IC category. Increasing age was negatively associated with high IC for older men and women. Older people who smoke tobacco (β = -0.23; CI: -0.32--0.13) and chew tobacco (β = -0.11; CI: -0.18--0.03) were less likely to experience high IC compared to their respective counterparts. Older adults who reported episodic alcohol drinking were less likely to have high IC (β = -0.20; CI:-0.32--0.07). The engagement in moderate physical activity (β = 0.12; CI:0.01-0.23), vigorous physical activity (β = 0.12; CI:0.05-0.20) and yoga-related activity (β = 0.18; CI:0.09-0.26) were significantly positively associated with high IC. Among the five domains of IC, education was significantly associated with higher capacity in each domain, and increasing age was found to be a significant predictor of lower capacity in each IC domain except locomotion. Older men and women engaged in vigorous physical activity had 35 and 19% significantly higher odds of high capacity in sensory (aOR = 1.35; CI: 1.12-1.62) and psychological (aOR = 1.19; CI: 1.06-1.34) domains, respectively.ConclusionsThe study revealed that lifestyle behaviours including tobacco use, episodic alcohol drinking and physical activity are strongly associated with IC among older adults in India. The findings suggest that healthy lifestyle behaviours should be encouraged among older adults as an effort to improve their IC, which is the key determinant of functional ability and quality of life in later years of life.
| S-EPMC9652958 | biostudies-literature
Project description:Wearable activity trackers are newly emerging technologies with the anticipation for successfully supporting aging-in-place. Consumer-grade wearable activity trackers are increasingly ubiquitous in the market, but the attitudes toward, as well as acceptance and voluntary use of, these trackers in older population are poorly understood.The aim of this study was to assess acceptance and usage of wearable activity trackers in Canadian community-dwelling older adults, using the potentially influential factors as identified in literature and technology acceptance model.A mixed methods design was used. A total of 20 older adults aged 55 years and older were recruited from Southwestern Ontario. Participants used 2 different wearable activity trackers (Xiaomi Mi Band and Microsoft Band) separately for each segment in the crossover design study for 21 days (ie, 42 days total). A questionnaire was developed to capture acceptance and experience at the end of each segment, representing 2 different devices. Semistructured interviews were conducted with 4 participants, and a content analysis was performed.Participants ranged in age from 55 years to 84 years (mean age: 64 years). The Mi Band gained higher levels of acceptance (16/20, 80%) compared with the Microsoft Band (10/20, 50%). The equipment characteristics dimension scored significantly higher for the Mi Band (P<.05). The amount a participant was willing to pay for the device was highly associated with technology acceptance (P<.05). Multivariate logistic regression with 3 covariates resulted in an area under the curve of 0.79. Content analysis resulted in the formation of the following main themes: (1) smartphones as facilitators of wearable activity trackers; (2) privacy is less of a concern for wearable activity trackers, (3) value proposition: self-awareness and motivation; (4) subjective norm, social support, and sense of independence; and (5) equipment characteristics matter: display, battery, comfort, and aesthetics.Older adults were mostly accepting of wearable activity trackers, and they had a clear understanding of its value for their lives. Wearable activity trackers were uniquely considered more personal than other types of technologies, thereby the equipment characteristics including comfort, aesthetics, and price had a significant impact on the acceptance. Results indicated that privacy was less of concern for older adults, but it may have stemmed from a lack of understanding of the privacy risks and implications. These findings add to emerging research that investigates acceptance and factors that may influence acceptance of wearable activity trackers among older adults.
| S-EPMC5707431 | biostudies-literature
Project description:Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
| S-EPMC8659489 | biostudies-literature
Project description:Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a "count" based, device specific method.
| S-EPMC4646630 | biostudies-literature
Project description:Introduction: Increasing physical activity is associated with health benefits. Reduced physical activity has been noted in sarcoidosis, particularly where fatigue co-exists. Monitoring physical activity is possible with wrist-worn devices. This study compared two available devices to determine patient preference and compare wear-time, with a secondary outcome of comparing device outputs with fatigue scores. Methods: Patients with sarcoidosis wore two wrist-worn activity monitors (GENEActiv actiwatch and Actigraph GT3X-bt) separately for seven days each. Participants were randomly allocated to receive either device first. Participants completed the Fatigue Assessment Scale (FAS) questionnaire immediately before wearing the first device. All participants completed a questionnaire of their perception regarding each device after the wear period. Data from the devices was analysed for total wear time, time spent in moderate or vigorous activity (MVPA) and for time spent in sedentary behaviours. Results: Twelve patients with sarcoidosis were included. The GENEActiv device was preferred by ten (83.3%) participants. Wear time was greater with the GENEActiv device (1354 minutes/day vs 1079 minutes/day). Time spent in MVPA was slightly higher when recorded by the GENEActiv compared with the Actigraph. Moderately strong correlation was seen between FAS scores and sedentary time (r=-0.554), light activity (r=-0.585) and moderate activity (r=0.506). Discussion: A clear preference was demonstrated for the GENEActiv. This was reflected in higher wear time and suggests the device can be comfortably worn 24 hours per day. Data from this small cohort also suggests there is correlation between fatigue and activity scores in patients with sarcoidosis. (Sarcoidosis Vasc Diffuse Lung Dis 2018; 35: 62-68).
| S-EPMC7170060 | biostudies-literature