Prime Time 3 Level

[Chris Domino]

Withthe promotion of physical activity in young people, an established global health priority [1], it is critically important for governments and public health agencies to have a clear understanding of the proportion of children and adolescents meeting guidelines for physical activity and sedentary behaviour. Historically, the assessment of physical activity in large scale population-level surveillance systems has been limited to self-report measures. However, among youth, self-report methods are subject to significant social desirability and recall bias [2, 3]. Younger children, in particular, have difficulty recalling their past behaviour accurately; and struggle to understand the concepts of physical activity frequency, intensity, duration, and type [4]. Proxy self-reports completed by parents or caregivers are one solution, but this method is also subject to recall bias since respondents can only report on the time they are in contact with the child [2, 3]. In light of the limitations of self-report methods, device based physical activity measures such as accelerometers have become the preferred method in studies involving children and young people [2, 5].

Despite the ubiquitous use of accelerometer-based motion sensors in physical activity studies involving children and adolescents, the application of accelerometers in population-level physical activity surveillance systems has been seriously questioned [6]. Citing methodological limitations related to questionable validity, between-monitor variability, multiple sets of conflicting intensity-based cut-points, and bias resulting from monitoring non-compliance, Pedisic and Bauman [6] concluded that without appropriate standardisation protocols, the adoption of accelerometers for large-scale physical activity surveillance was premature. They further concluded that accelerometer-based physical activity measures should not substitute, but only supplement self-report information systems; and that self-report methods should be the primary assessment tool in physical activity surveillance systems.

A significant limitation of the Steene-Johannessen study is the reliance on proprietary count-based metrics - making it impossible to integrate data from studies deploying different brands of accelerometers. The solution to this problem is to discontinue the practice of calculating and reporting count-based metrics and applying the currently available methods and metrics based on the raw acceleration signal [8,9,10]. It is acknowledged that Steene-Johannessen included data from studies that were conducted long before raw accelerometer data from the ActiGraph could be readily accessed, limiting their ability to use this approach. However, if future accelerometer data pooling projects are to provide legitimate between-study and cross-country comparability, the more than two decade old practice of applying cut-points to processed activity counts must be phased out.

In the Pedisic and Bauman review [6], the existence of multiple sets of conflicting intensity-based cut-points was identified as a major methodological weakness limiting the application of accelerometers in population-level surveillance studies. Steene-Johannessen and colleagues partially address this issue by applying, in a standardised manner, an intensity-based cut-point with established evidence of validity in school-aged youth [11, 12]. However, there is growing recognition that the relationship between accelerometer counts and energy expenditure is highly dependent on the activities included the calibration study; and that cut-points derived from a single regression model or Receiver Operating Characteristic curve cannot adequately characterise physical activity intensity across a wide range of physical activities [13]. In an independent evaluation of ActiGraph cut-points for youth, the Evenson thresholds were found to have the least physical activity intensity classification error of all the cut-points tested [12]. However, it is important to note that the Evenson cut-points still misclassified MVPA as light-intensity physical activity 20% of the time, and that light intensity physical activities were misclassified as sedentary at least 40% of the time [12]. Moreover, given that the relationship between activity counts and energy expenditure in children under five differs substantially to that observed in adolescent youth [14], the application of the Evenson cut-points in children aged 2- to 5-years by Steene-Johannessen must be questioned.

Over the last decade, there has been a shift from count-based thresholds to machine learning activity classification and energy expenditure estimation algorithms based on features extracted from raw accelerometer signals [15]. When applied to youth, machine learning approaches have shown to provide more accurate predictions of physical activity intensity [13, 16]. Moreover, in contrast to cut-point methods, which only estimate time spend in MVPA, physical activity classification models can predict time spent in specific activity types (e.g., walking, running, dancing, cycling) or broader activity classes (e.g., active games or sports) [13, 16]. This enables researchers in the public health and exercise sciences to explore a greater variety of physical activity metrics as well as examine age-related differences in movement behaviours that are not confounded by developmental differences in the relationship between accelerometer counts and energy expenditure.

To date, the uptake of machine learning methods by public health researchers has been slow, primarily because of the need to collect and process large quantities of raw accelerometer signal using specialised software; and partly because of concerns that machine learning models trained on laboratory-based activities trials do not generalise well to free living scenarios [17]. As machine learning accelerometer data processing methods evolve and the required computer platforms enabling public health researchers to apply machine learning methods become available, it is anticipated that future physical activity surveillance studies will address the aforementioned limitations of cut-point methods by implementing potentially more accurate and versatile machine learning accelerometer data processing methods.

The article by Steene-Johannessen and colleagues [7] highlights the utility of accelerometer-based measures in physical activity surveillance studies involving children and adolescents. The findings are significant and identify the promotion of physical activity as a priority concern for public health authorities across Europe. Yet, it is important to acknowledge that the accelerometer data processing methods applied in this study have been implemented with only minor modifications for more than two decades, despite significant advances in wearable sensor technology and artificial intelligence over the same time period. If accelerometer-based measures of physical activity and sedentary behaviour are to be accepted as best practice methodology in large scale population-level surveillance studies, public health researchers must be willing to adopt more contemporary monitoring protocols and apply new accelerometer data processing methods.

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The author Sam Carpenter described the biological prime time method in his book Work the System. According to Carpenter, the gist of the method is to work smart, instead of hard, and the method is aimed at boosting self-development.

Your chart will basically look the same, including your energy, focus, and motivation on a 1 to 10 scale. However, gathering more data will ensure that you are actually identifying your peak hours accurately and precisely to the hour, instead of only for different sections of the day.

Usually, if my energy was low, I would opt for a break, or do a low-priority task. Finding out when my focus is really high at the same time has shifted my perception of such periods. I tried completing some of the most important tasks during these times, and guess what? I accomplished those tasks more efficiently and in less time.

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