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Measuring infants’ (0–2 years) physical activity is a growing area of research globally. Accelerometers have been widely used to measure older children’s and adults’ physical activity. An increasing number of studies have used accelerometers as a way to measure infant physical activity, which has resulted in the application of a variety of methods. The purpose of this scoping review is to synthesize the published literature on accelerometer methodology to measure daytime physical activity among infants (0–2 years). A systematic search of five online databases using carefully selected key terms was conducted to compile relevant literature. The results of the online database searches were screened for inclusion in the scoping review. In total, 105 articles met the inclusion criteria of using accelerometers to measure infants’ physical activity. The methodologies used in the included studies were categorized by age groups: <1 month, 1–6 months, >6–12 months, >12–18 months, >18–24 months, and longitudinal (i.e., multiple measurements taken across the previously mentioned age groups). Accelerometry methodologies (e.g., wear location, number of devices, device initialization) and study design qualities (e.g., outcome of interest and location of data collection) varied widely between and within the various age groups. Accelerometer brand or type of device demonstrated greatest variation across included studies. However, ActiGraph devices to measure physical activity within free-living environments were the most common. This review provides evidence of the need for researchers to ensure the methodology used is reported in detail in order to help develop methodology that can accurately assess infant daytime movement.

Accelerometers have been at the forefront of free-living activity capture for decades, and accordingly ActiGraph the largest distributor. Historically, limitations in data storage and battery power led to the use of summary metrics, which have been termed activity counts. Recently, ActiGraph publicly released their count-based algorithm, marking a notable development in the field. This study aimed to assess and compare activity counts generated through different processing techniques (ActiLife and open-source), filters that are available through ActiGraph count generation (normal- and low-frequency extension), and data from various ActiGraph models and GENEActiv devices. We evaluated ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and secondary sensors), OPAL (n = 6), and GENEActiv (n = 5), subjected to oscillations across their full dynamic range (0.005–8 G) using a multiaxis shaker table. Results indicated that the low-frequency extension produced significantly higher counts compared to the normal frequency across the devices and processing techniques. Notably, open-source counts (R and Python) were statistically equivalent to ActiLife-generated counts (p < .05) for the GT9X, wGT3X-BT, and the GT3X+. Overall, many of the counts generated by different ActiGraph models were statistically equivalent or had mean differences <5.03 counts. Conversely, the GENEActiv, OPAL, and GT9X secondary monitor exhibited significantly higher responses than the other ActiGraph models at higher frequencies with mean differences ranging from 55.50 to 104.91 counts. This study provides insights into accelerometer data processing methods and highlights the comparability of counts across different devices and techniques.

Activity counts have been used for over two decades with over 22,000 published scientific papers in public health and clinical research. ActiGraph recently released the algorithm for computing counts from raw accelerometer data as an open-source Python library, which is now ported by researchers to other languages, notably R. The current commentary presents historical overview of ActiGraph counts, and its development and evolution as a measure of physical activity. Further, we provide general recommendations on extracting counts from raw accelerometer data and discuss specific considerations with respect to device types, resampling, nonwear, axes orientations, and epoch length that may influence counts. Last, we provide a tutorial on how to use ActiGraph’s open-source Python library, agcounts, for consistent, accurate, and reproducible count. We expect this commentary will provide familiarity and transparency needed to adopt and produce activity counts in a consistent manner, allowing researchers to conduct statistical comparisons across multiple data sets and studies.

Background: Against the backdrop of China’s active response to population aging, an increasing number of older adults are participating in leisure sports activities to enrich later life and experience active aging. However, when participating in these activities, older adults encounter constraints that affect their leisure sports behaviors. Methods: To understand the current state of leisure constraints, leisure sports behaviors, and active aging among Chinese seniors, we collected 1,581 surveys from older adults in Zhejiang Province, China. A stepwise regression model was used to explore the relationships between leisure constraints and leisure sports behavior as well as between leisure sports behavior and active aging. Results: The respondents’ leisure sports were characterized by light-intensity, moderate duration, high-frequency, and long-term participation with various leisure constraints, such as a lack of organized leisure sports activities. However, the respondents reported that their aging status was positive. Leisure constraints were negatively associated with leisure sports behavior, and self-constraint had a significant impact on leisure sports behavior. Leisure sports behavior positively affected the active aging of older participants and was positively associated with the four subdimensions of active aging: health, participation, security, and lifelong learning. Conclusion: High-frequency and long-term participation in leisure sports is an effective strategy for Chinese seniors to promote active aging. However, there are still many constraints that limit the leisure sports behaviors of older adults. Implications: The findings may inspire Chinese older adults to achieve active aging through leisure sports and provide support for the literature.

The aim of this study was to determine the psychometric properties of the pictorial scale of Perceived Movement Skill Competence for Turkish children. A total of 255 children aged 5–10 years (131 girls and 124 boys) participated in this study. One week later, a random subsample (n = 40) was implemented the scale again for test–retest reliability. We conducted confirmatory factor analysis to test the construct validity of the Turkish version of the scale and calculated the omega (ω) internal consistency and intraclass correlation coefficients (ICCs) to ascertain reliabilities of the subscales. The confirmatory factor analysis showed good fit index values for the model with 13 items indicating a similar factor structure to the original scale’s two-factor structure (χ ²/df = 1.56; root mean square error of approximation  = .048; Tucker–Lewis index  = .97; comparative-fit index  = .97). The test–retest reliabilities were excellent for the perceived locomotor (ICC = .95) and object control (ICC = .93) skills. An adequate internal consistency coefficient was found for locomotor (ω = 0.69) and object control (ω = 0.82) skills. Subsequent analyses supported the construct, metric, partial scalar and strict invariance of responses to the scale as a function of sex. Overall, the pictorial scale of Perceived Movement Skill Competence has acceptable psychometric properties and can be confidently used in research or practice to assess Turkish children’s perceived movement skill competence.

Background/Objective: This study aims to analyze the effect of whey protein (WP) supplementation on glycemic control, muscle strength, quality of life, and body composition in older adults with non-insulin-dependent diabetes undergoing a resistance training program. Methods: A double-blind, placebo-controlled, randomized study was carried out with older adults with Type 2 diabetes. Body composition, food intake, muscle strength, glycemic profile, markers of renal function, cardiopulmonary capacity, and quality of life were evaluated. Older adults were randomized into a supplementation group with 33 g of WP consumed twice a week on days of resistance training for 12 weeks supplemented group and a placebo group. Results: In total, 39 older adults were randomized into two groups, 19 in the supplement group and 20 in the placebo group. Both groups showed improvement in muscle strength, with the WP group failing to exceed that of the control group. WP supplementation slightly increased blood urea compared with the placebo group (p = .05), but values remained within normal limits. The group that used WP showed a small improvement in mental health, according to the 12-Item Short-Form Health Survey questionnaire, but without clinically important improvements. Conclusion: Both groups showed improvements in muscle strength and mass, regardless of supplementation, showing that resistance exercises performed twice a week can contribute to the nonprogression of sarcopenia in older adults with Type 2 diabetes. More studies are needed to determine the safe and effective amount of supplementation to improve muscle strength without renal impairment in older adults with diabetes.

Older adults living in retirement communities are an understudied population, and the association between their motivation and daily physical activity is unknown. We recruited participants (n = 173) living in a retirement community who completed the Behavioral Regulation in Exercise Questionnaire-2 and wore an activPAL accelerometer to evaluate this relationship. Participants had a median age of 81 years and demonstrated low levels of daily activity with an average step count of 3,637 (±1,965) steps per day and 52 (±25) min of daily stepping time. External motivation was negatively associated with the square root of daily step count (β = −4.57; p < .001) and square root of daily stepping time (β = −0.49; p < .001). Older adults living in retirement communities demonstrated low levels of daily activity, with a negative association between external motivation and daily activity. Strategies are needed to make an active lifestyle supportive and enticing for older adults in these communities.