Surprisingly no use was made of time series analysis for analysing step count data .
The review also suggested several strategies for the personalisation of physical activity programs .
Conclusions Overall , it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial , demographic , weather and climatic factors .
Statistical models can provide a better underst and ing of the impact of these factors , allowing for the provision of more personalised physical activity programs , which are expected to produce better immediate and long-term outcomes for participants .
Background Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle .
Regular engagement in physical activity can produce immediate and long term health benefits .
However , physical activity levels are not as high as might be expected .
For example , according to the global World Health Organization ( WHO ) 2017 statistics , more than 80 % of the world ’s adolescents are insufficiently physically active .
In response to this problem , physical activity programs have become popular , with step counts commonly used to measure program performance .
Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance .
This study review s the statistical methods that are used to model and evaluate physical activity programs , using step counts .
Testing self-regulation interventions to increase walking using factorial randomized N-of-1 trials.
OBJECTIVE To investigate the suitability of N-of-1 r and omized controlled trials ( RCTs ) as a means of testing the effectiveness of behavior change techniques based on self-regulation theory ( goal setting and self-monitoring ) for promoting walking in healthy adult volunteers . METHOD A series of N-of-1 RCTs in 10 normal and overweight adults ages 19 - 67 ( M = 36.9 years ) . We r and omly allocated 60 days within each individual to text message-prompted daily goal - setting and /or self-monitoring interventions in accordance with a 2 ( step-count goal prompt vs. alternative goal prompt ) × 2 ( self-monitoring : open vs. blinded Omron-HJ-113-E pedometer ) factorial design . Aggregated data were analyzed using r and om intercept multilevel models . Single cases were analyzed individually . The primary outcome was daily pedometer step counts over 60 days . RESULTS Single-case analyses showed that 4 participants significantly increased walking : 2 on self-monitoring days and 2 on goal - setting days , compared with control days . Six participants did not benefit from the interventions . In aggregated analyses , mean step counts were higher on goal - setting days ( 8,499.9 vs. 7,956.3 ) and on self-monitoring days ( 8,630.3 vs. 7,825.9 ) . Multilevel analyses showed a significant effect of the self-monitoring condition ( p = .01 ) , the goal - setting condition approached significance ( p = .08 ) , and there was a small linear increase in walking over time ( p = .03 ) . CONCLUSION N-of-1 r and omized trials are a suitable means to test behavioral interventions in individual participants
Surprisingly no use was made of time series analysis for analysing step count data .
The review also suggested several strategies for the personalisation of physical activity programs .
Conclusions Overall , it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial , demographic , weather and climatic factors .
Statistical models can provide a better underst and ing of the impact of these factors , allowing for the provision of more personalised physical activity programs , which are expected to produce better immediate and long-term outcomes for participants .
Background Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle .
Regular engagement in physical activity can produce immediate and long term health benefits .
However , physical activity levels are not as high as might be expected .
For example , according to the global World Health Organization ( WHO ) 2017 statistics , more than 80 % of the world ’s adolescents are insufficiently physically active .
In response to this problem , physical activity programs have become popular , with step counts commonly used to measure program performance .
Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance .
This study review s the statistical methods that are used to model and evaluate physical activity programs , using step counts .