How many days are needed? Measurement reliability of wearable device data to assess physical activity

DE-PASS best evidence statement (BESt): determinants of adolescents' device-based physical activity and sedentary behaviour in settings: a systematic review and meta-analysis

BACKGROUND

Although physical activity (PA) is associated with significant health benefits, only a small percentage of adolescents meet recommended PA levels. This systematic review with meta-analysis explored the modifiable determinants of adolescents' device-based PA and/or sedentary behaviour (SB), evaluated in previous interventions and examined the associations between PA/SB and these determinants in settings.

METHODS

A search was conducted on five electronic databases, including papers published from January 2010 to July 2023. Randomized Controlled Trials (RCTs) or Controlled Trials (CTs) measuring adolescents' device-based PA/SB and their modifiable determinants at least at two time points: pre- and post-intervention were considered eligible. PA/SB and determinants were the main outcomes. Modifiable determinants were classified after data extraction adopting the social-ecological perspective. Robust Bayesian meta-analyses (RoBMA) were performed per each study setting. Outcomes identified in only one study were presented narratively. The risk of bias for each study and the certainty of the evidence for each meta-analysis were evaluated. The publication bias was also checked.

PROSPERO ID

CRD42021282874.

RESULTS

Fourteen RCTs (eight in school, three in school and family, and one in the family setting) and one CT (in the school setting) were included. Fifty-four modifiable determinants were identified and were combined into 33 broader determinants (21 individual-psychological, four individual-behavioural, seven interpersonal, and one institutional). RoBMAs revealed none or negligible pooled intervention effects on PA/SB or determinants in all settings. The certainty of the evidence of the impact of interventions on outcomes ranged from very low to low. Narratively, intervention effects in favour of the experimental group were detected in school setting for the determinants: knowledge of the environment for practicing PA, d = 1.84, 95%CI (1.48, 2.20), behaviour change techniques, d = 0.90, 95%CI (0.09, 1.70), choice provided, d = 0.70, 95%CI (0.36, 1.03), but no corresponding effects on PA or SB were found.

CONCLUSIONS

Weak to minimal evidence regarding the associations between the identified modifiable determinants and adolescents' device-based PA/SB in settings were found, probably due to intervention ineffectiveness. Well-designed and well-implemented multicomponent interventions should further explore the variety of modifiable determinants of adolescents' PA/SB, including policy and environmental variables.

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

BACKGROUND

Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data.

METHODS

Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis.

RESULTS

Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization.

CONCLUSIONS

Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

Does Wrist-Worn Accelerometer Wear Compliance Wane over a Free-Living Assessment Period? An NHANES Analysis

PURPOSE

Accelerometers are used to objectively measure physical behaviors in free-living environments, typically for seven consecutive days or more. We examined whether participants experience "wear fatigue," a decline in wear time day over day, during typical assessment period acquired in a nationally representative sample of 6- to 80-yr-olds in the United States.

METHODS

Participants were instructed to wear an ActiGraph GT3X+ on their nondominant wrist continuously for seven consecutive days. Participants with seven complete days of recorded data, regardless of wear status, were included in the analyses ( N = 13,649). Wear was scored with the sleep, wake, and nonwear algorithm.

RESULTS

Participants averaged 1248 ± 3.6 min·d -1 (mean ± SE) of wear over the assessment, but wear time linearly decreased from day 1 (1295 ± 3.2 min) to day 7 (1170 ± 5.3 min), resulting in a wear fatigue of -18.1 ± 0.7 min·d -1 ( β ± SE). Wear fatigue did not differ by sex but varied by age-group-highest in adolescents (-26.8 ± 2.4 min·d -1 ) and lowest in older adults (-9.3 ± 0.9 min·d -1 ). Wear was lower in evening (1800-2359 h) and early morning (0000-0559 h) compared with the middle of the day and on weekend days compared with weekdays. We verified similar wear fatigue (-23.5 ± 0.7 min·d -1 ) in a separate sample ( N = 14,631) with hip-worn devices and different wear scoring. Applying minimum wear criteria of ≥10 h·d -1 for ≥4 d reduced wear fatigue to -5.3 and -18.7 min·d -1 for the wrist and hip, respectively.

CONCLUSIONS

Patterns of wear suggest noncompliance may disproportionately affect estimates of sleep and sedentary behavior, particularly for adolescents. Further study is needed to determine the effect of wear fatigue on longer assessments.

The Efficacy of Wearable Cueing Devices on Gait and Motor Function in Parkinson Disease: A Systematic Review and Meta-analysis of Randomized Controlled Trials

OBJECTIVE

To summarize the efficacy of wearable cueing devices for improving gait and motor function of patients with Parkinson disease (PWP).

DATA SOURCES

PubMed, Embase, and Cochrane CENTRAL databases were searched for papers published in English, from inception to October 23, 2022.

STUDY SELECTION

Randomized controlled trials focusing on the effects of wearable cueing devices on gait and motor function in PWP were included.

DATA EXTRACTION

Two reviewers independently selected articles and extracted the data. The Cochrane Bias Risk Assessment Tool was used to assess risk of bias and the Grading of Recommendations Assessment, Development and Evaluation was used to evaluate the quality of evidence.

DATA SYNTHESIS

Seven randomized controlled trials with 167 PWP were included in the meta-analysis. Significant effect of wearable cueing devices on walking speed (mean difference [MD]=0.07 m/s, 95% confidence interval [CI]: [0.05, 0.09], P<.00001) was detected; however, after sensitivity analysis, no significant overall effect on walking speed was noted (MD=0.04 m/s, 95% CI: [-0.03, 0.12], P=.25). No significant improvements were found in stride length (MD=0.06 m, 95% CI: [0.00, 0.13], P=.05), the Unified Parkinson's Disease Rating Scale-III score (MD=-0.61, 95% CI: [-4.10, 2.88], P=.73), Freezing of Gait Questionnaire score (MD=-0.83, 95% CI: [-2.98, 1.33], P=.45), or double support time (MD=-0.91, 95% CI: [-3.09, 1.26], P=.41). Evidence was evaluated as low quality.

CONCLUSIONS

Wearable cueing devices may result in an immediate improvement on walking speed; however, there is no evidence that their use results in a significant improvement in other gait or motor functions.

Impact of habitual physical activity and exercise capacity on quality of life in adolescents and adults with cystic fibrosis

BACKGROUND

The influence of habitual physical activity and exercise capacity on health-related quality of life (HRQoL) in people with cystic fibrosis (pwCF) is poorly characterized. This study investigated the influence of habitual physical activity, exercise capacity, lung function, and body mass index (BMI) on HRQoL in adolescent and adult pwCF.

METHOD

Subjects were fitted with an accelerometer to determine habitual physical activity (steps/day), including time spent at different intensities, for up to 4 weeks. Then bicycle ergometry (maximal exercise capacity; Wpeak), lung function (percent predicted forced expiratory volume in 1 s, ppFEV1 ), BMI, and response to the Cystic Fibrosis Questionnaire-Revised (CFQ-R) were determined.

RESULTS

Sixty-five pwCF participated in the study. Physically active pwCF had significantly higher ppFEV1 (p < .001) and exercise capacity (p < .001) than inactive pwCF, and had significantly higher scores on the CFQ-R physical (p = .006), emotional (p = .015), role (p = .008), health (p = .006), and weight (p = .004) subscales. On multiple linear regression analysis, ppFEV1 and, to a lesser extent, exercise capacity, were the most important determinants of HRQoL in pwCF. Time spent in moderate-to-vigorous intensity physical activity did not influence any of the CFQ-R subscales, whereas time spent in vigorous-intensity influenced CFQ-R scores for role (p = .007), body (p = .001), health (p = .009), and weight (p = .01).

CONCLUSION

HRQoL in adolescent and adult pwCF was influenced by several factors. Avoiding sedentary behavior and spending time in vigorous-intensity levels positively influenced HRQoL, whereas the total number of steps per day played only a minor role in determining HRQoL. Both ppFEV1 and exercise capacity markedly influenced HRQoL.

Measuring intra-individual physical activity variability using consumer-grade activity devices

Many existing sedentary behavior and physical activity studies focus on primary outcomes that assess change by comparing participants' activity from baseline to post-intervention. With the widespread availability of consumer-grade devices that track activity daily, researchers do not need to rely on those endpoint measurements alone. Using activity trackers, researchers can collect remote data about the process of behavior change and future maintenance of the change by measuring participants' intra-individual physical activity variability. Measuring intra-individual physical activity variability can enable researchers to create tailored and dynamic interventions that account for different physical activity behavior change trajectories, and by that, improve participants' program adherence, enhance intervention design and management, and advance interventions measurements' reliability. We propose an application of intra-individual physical activity variability as a measurement and provide three use cases within interventions. Intra-individual physical activity variability can be used: prior to the intervention period, where relationships between participants' intra-individual physical activity variability and individual characteristics can be used to predict adherence and subsequently tailor interventions; during the intervention period, to assess progress and subsequently boost interventions; and after the intervention, to obtain a reliable representation of the change in primary outcome.