No abstract

Accelerometers are commonly used in clinical and epidemiological research for more detailed measures of physical activity and to target the limitations of self‐report methods. Sensors are attached at the hip, wrist and thigh, and the acceleration data are processed and calibrated in different ways to determine activity intensity, body position and/or activity type. Simple linear modelling can be used to assess activity intensity from hip and thigh data, whilst more advanced machine‐learning modelling is to prefer for the wrist. The thigh position is most optimal to assess body position and activity type using machine‐learning modelling. Frequency filtering and measurement resolution needs to be considered for correct assessment of activity intensity. Simple physical activity measures and statistical methods are mostly used to investigate relationship with health, but do not take advantage of all information provided by accelerometers and do not consider all components of the physical activity behaviour and their interrelationships. More advanced statistical methods are suggested that analyse patterns of multiple measures of physical activity to demonstrate stronger and more specific relationships with health. However, evaluations of accelerometer methods show considerable measurement errors, especially at individual level, which interferes with their use in clinical research and practice. Therefore, better objective methods are needed with improved data processing and calibration techniques, exploring both simple linear and machine‐learning alternatives. Development and implementation of accelerometer methods into clinical research and practice requires interdisciplinary collaboration to cover all aspects contributing to useful and accurate measures of physical activity behaviours related to health.

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.

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.

In this group of childhood brain tumour survivors, home-based AVG, supported by a coach, was a feasible, enjoyable and moderately intense form of exercise that improved Body Coordination. Implications for Rehabilitation Childhood brain tumour survivors frequently have cognitive problems, inferior physical functioning and are less physically active compared to their healthy peers. Active video gaming (AVG), supported by Internet coaching, is a feasible home-based intervention in children treated for brain tumours, promoting enjoyable, regular physical exercise of moderate intensity. In this pilot study, AVG with Nintendo Wii improved Body Coordination.

ActiGraph is the most common accelerometer in physical activity research, but it has measurement errors due to restrictive frequency filtering. This study investigated biomechanically how different frequency filtering of accelerometer data affects assessment of activity intensity and age-group differences when measuring physical activity. Data from accelerometer at the hip and motion capture system was recorded during treadmill walking and running from 30 subjects in three different age groups: 10, 15, and >20 years old. Acceleration data was processed to ActiGraph counts with original band-pass filter at 1.66 Hz, to counts with wider filter at either 4 or 10 Hz, and to unfiltered acceleration according to “Euclidian norm minus one” (ENMO). Internal and external power, step frequency, and vertical displacement of center of mass (VD) were estimated from the motion capture data. Widening the frequency filter improved the relationship between higher locomotion speed and counts. It also removed age-group differences and decreased within-group variation. While ActiGraph counts were almost exclusively explained by VD, the counts from the 10 Hz filter were explained by VD and step frequency to an equal degree. In conclusion, a wider frequency filter improves assessment of physical activity intensity by more accurately capturing individual gait patterns.

ObjectivesThis study investigates the effects of the core elements of the Swedish model for physical activity on prescription (PAP) by evaluating studies that compared adults who received PAP with adults who did not receive PAP. All participants were adults identified by a healthcare professional as in need of increased physical activity. Primary outcome was level of physical activity.DesignSystematic review.Eligibility criteria(1) Published 1999. (2) Systematic review, randomised controlled trial (RCT), non-RCT or case series (for adverse events). (3) ≥12 weeks’ follow-up. (4) Performed in the Nordic countries. (5) Presented in English, Swedish, Norwegian or Danish.Data sourcesSystematic searches in PubMed, Embase, the Cochrane Library, AMED, CINAHL and SweMed+ in September 2017. Included articles were evaluated using checklists to determine risk of bias.ResultsNine relevant articles were included: seven RCTs, one cohort study and one case series. Primary outcome was reported in seven articles from six studies (five RCTs, one cohort study, 642 participants). Positive results were reported from three of the five RCTs and from the cohort study. No study reported any negative results. Swedish PAP probably results in an increased level of physical activity (GRADE⊕⊕⊕Ο).ConclusionsAlthough the number of the reviewed articles was relatively modest, this systematic review shows that PAP in accordance with the Swedish model probably increases the level of physical activity. As a model for exercise prescription, Swedish PAP may be considered as part of regular healthcare to increase physical activity in patients.

In objective physical activity (PA) measurements, applying wider frequency filters than the most commonly used ActiGraph (AG) filter may be beneficial when processing accelerometry data. However, the vulnerability of wider filters to noise has not been investigated previously. This study explored the effect of wider frequency filters on measurements of PA, sedentary behavior (SED), and capturing of noise. Apart from the standard AG band-pass filter (0.29–1.63 Hz), modified filters with low-pass component cutoffs at 4 Hz, 10 Hz, or removed were analyzed. Calibrations against energy expenditure were performed with lab data from children and adults to generate filter-specific intensity cut-points. Free-living accelerometer data from children and adults were processed using the different filters and intensity cut-points. There was a contribution of acceleration related to PA at frequencies up to 10 Hz. The contribution was more pronounced at moderate and vigorous PA levels, although additional acceleration also occurred at SED. The classification discrepancy between AG and the wider filters was small at SED (1–2%) but very large at the highest intensities (>90%). The present study suggests an optimal low-pass frequency filter with a cutoff at 10 Hz to include all acceleration relevant to PA with minimal effect of noise.