Background: Childhood represents an important life stage for establishment of physical activity (PA) habits. Parents are assumed to play an important role in influencing children’s PA. Earlier reviews have mainly focused on parental modelling, encouragement, and support for PA, rather than the actual PA levels of parents. Therefore, the purpose of this review was to systematically summarize the evidence on the relationship between parent and child PA.Methods: Papers were identified using electronic databases and manual searches of reference lists. Papers reporting on associations between objectively measured child PA and at least one measure of parental PA were included. The quality of the papers was assessed using a modified version of the ROBINS-I tool. For interpretation of the results across studies, we produced albatross plots for all studies combined and by age-groups, sex of the parents, sex of the child, methodology of assessment of parental PA, and type of PA.Results: Thirty-nine papers were included with sample size of parent-child dyads ranging from 15 to 1267 (mean = 319 dyads, median = 227 dyads). The majority of studies were published from 2008 to 2018 and used accelerometry to assess PA. Most of the studies were classified as having moderate, serious, or critical risk of bias. The albatross plot for all studies combined showed that the clear majority of studies observed a positive relationship between parent and child PA. The plot suggested an average magnitude of correlation across studies to be around 0.13, and the overall impression was that this was fairly similar across child age-groups and gender of parent-child dyads. Studies using objective assessment of parental PA showed stronger relationship between parent and child PA compared with studies using self-report (average magnitude of correlation around 0.16 vs 0.04 respectively). No clear evidence was found for the strength of relationship being dependent on type of PA measure of parent and child (total PA, moderate-to-vigorous PA, steps), however, the relationship for light PA appeared weaker.Conclusion: This systematic review showed that the clear majority of studies observed a weak positive relationship between parent and child PA regardless of age of the child, the gender of the parent-child dyad, and type of PA.Trial registration: Registration in PROSPERO:CRD42019093462.

This study aimed to implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. The aggregation method, including the frequency band-pass filter, was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-h free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared with counts generated with ActiLife from ActiGraph GT3X+ data. An optimal band-pass filter was fitted resulting in a root-mean-square error of 25.7 counts per 10 s and mean absolute error of 15.0 counts per second across the full frequency range. The mechanical evaluation of the proposed aggregation method resulted in an absolute mean ± SD difference of −0.11 ± 0.97 counts per 10 s across all rotational frequencies compared with the original ActiGraph method. Applying the aggregation method to the 24-h free-living recordings resulted in an epoch level bias ranging from −16.2 to 0.9 counts per 10 s, a relative difference in the averaged physical activity (counts per minute) ranging from −0.5% to 4.7% with a group mean ± SD of 2.2% ± 1.7%, and a Cohen’s kappa of 0.945, indicating almost a perfect agreement in the intensity classification. The proposed band-pass filter and aggregation method is highly valid for generating ActiGraph counts from raw acceleration data recorded with alternative devices. It would facilitate comparability between studies using different devices collecting raw acceleration data.

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

ActiGraph acceleration data are processed through several steps (including band-pass filtering to attenuate unwanted signal frequencies) to generate the activity counts commonly used in physical activity research. We performed three experiments to investigate the effect of sampling frequency on the generation of activity counts. Ideal acceleration signals were produced in the MATLAB software. Thereafter, ActiGraph GT3X+ monitors were spun in a mechanical setup. Finally, 20 subjects performed walking and running wearing GT3X+ monitors. Acceleration data from all experiments were collected with different sampling frequencies, and activity counts were generated with the ActiLife software. With the default 30-Hz (or 60-Hz, 90-Hz) sampling frequency, the generation of activity counts was performed as intended with 50% attenuation of acceleration signals with a frequency of 2.5 Hz by the signal frequency band-pass filter. Frequencies above 5 Hz were eliminated totally. However, with other sampling frequencies, acceleration signals above 5 Hz escaped the band-pass filter to a varied degree and contributed to additional activity counts. Similar results were found for the spinning of the GT3X+ monitors, although the amount of activity counts generated was less, indicating that raw data stored in the GT3X+ monitor is processed. Between 600 and 1,600 more counts per minute were generated with the sampling frequencies 40 and 100 Hz compared with 30 Hz during running. Sampling frequency affects the processing of ActiGraph acceleration data to activity counts. Researchers need to be aware of this error when selecting sampling frequencies other than the default 30 Hz.

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.