Impact of Activity Tracker Usage in Combination with a Physical Activity Intervention on Physical and Cognitive Parameters in Healthy Adults Aged 60+: A Randomized Controlled Trial

Regular physical activity (PA) is of central importance for healthy aging and has a well-known impact on helping older adults maintain their cognitive and physical health. Thus, we aimed to compare the effectiveness of two physical activity interventions primarily conducted at home (print-based or web-based vs. web-based plus the use of an activity tracker) on cognitive and physical health parameters in older adults. Data of participants (n = 551, 60–80 years) were analyzed after being randomly allocated to a waitlist control group (CG), a web-based or print-based intervention group (IG) or a web-based intervention group that also included the use of an activity tracker (AG). Measured parameters were grip strength, endurance (two-minute step test), gait speed (four-meter walk test), cognition (Simon task; balanced integration score (BIS), reaction time and accuracy) and physical self-concept (Physical Self-Description Questionnaire (PSDQ)). We found the highest effect sizes in all measured dimensions for AG (grip strength, endurance, gait speed, reaction time, physical self-concept), followed by IG (endurance, gait speed, reaction time, physical self-concept) and CG (endurance, gait speed, BIS). Findings suggest that a combined web-based and activity tracker intervention may improve physical functions, physical self-concept, and cognition in community-dwelling older adults

Impact of Activity Tracker Usage in Combination with a Physical Activity Intervention on Physical and Cognitive Parameters in Healthy Adults Aged 60+: A Randomized Controlled Trial

Regular physical activity (PA) is of central importance for healthy aging and has a well-known impact on helping older adults maintain their cognitive and physical health. Thus, we aimed to compare the effectiveness of two physical activity interventions primarily conducted at home (print-based or web-based vs. web-based plus the use of an activity tracker) on cognitive and physical health parameters in older adults. Data of participants (n = 551, 60–80 years) were analyzed after being randomly allocated to a waitlist control group (CG), a web-based or print-based intervention group (IG) or a web-based intervention group that also included the use of an activity tracker (AG). Measured parameters were grip strength, endurance (two-minute step test), gait speed (four-meter walk test), cognition (Simon task; balanced integration score (BIS), reaction time and accuracy) and physical self-concept (Physical Self-Description Questionnaire (PSDQ)). We found the highest effect sizes in all measured dimensions for AG (grip strength, endurance, gait speed, reaction time, physical self-concept), followed by IG (endurance, gait speed, reaction time, physical self-concept) and CG (endurance, gait speed, BIS). Findings suggest that a combined web-based and activity tracker intervention may improve physical functions, physical self-concept, and cognition in community-dwelling older adults

Assessing physical behavior through accelerometry – State of the science, best practices and future directions

Accelerometers offer opportunities for researchers to capture validdata about the intensity and amount of physical behavior (PB) in real-time over a period of several days and weeks. From this multi-dimensional data, a great number of metrics can be derived to captureand describe the unique aspects of PB. The goal of this paper is to helpthe end-user of PB monitoring devices (novice to intermediate experi-ence) wade through sometimes excessive technical details of accel-erometry to outline best practices in selecting and applying devices toquantify three major behavioral categories of common interest to theresearch community: physical activity (PA), sedentary behavior (SB)and sleep. The effects of these decisions on the metrics (energy ex-penditure, activity intensity, body position, activity patterns) can occurin a variety of ways. The device, carrying position (hip, wrist, thigh)and recording parameters (epoch length (EL), frequency, memory ca-pacity, recording frequency andfilters) have a large influence on themeasured activity. The different backgrounds such as study design(purpose, repeated measurements) and duration (time frame, weartime) as well as data storage and evaluation must be taken into accountwhen determining the parameters. Finally, the evaluation must adjustseveral levers (raw data, context information, non-wear time, intensityclassification, compliance) depending on the target variables. Lookinginto the future, current developments in statistical analysis are dis-cussed, because the research community has not yet reached a con-sensus on the most promising approach. There are exciting develop-ments ahead of us in the future. Sleep in particular is increasingly beingseen as an influencing factor for health. Together with the technicaldevelopments in sensors which will become incrementally smaller,more accurate and in the near future will be integrated directly into ourclothes or skin, accelerometry is facing exciting times and lots of data toevaluate