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Evaluating the Population Health Impact of Physical Activity Interventions in Primary Care—Are We Asking the Right Questions?

Elizabeth G. Eakin, Ben J. Smith, and Adrian E. Bauman

Background:

This article evaluates the extent to which the literature on primary care-based physical activity interventions informs the translation of research into practice and identifies priorities for future research.

Methods:

Relevant databases were searched for: (1) descriptive studies of physician barriers to physical activity counseling (n = 8), and (2) reviews of the literature on primary care-based physical activity intervention studies (n = 9). The RE-AIM framework was used to guide the evaluation.

Results:

Lack of time, limited patient receptiveness, lack of remuneration, and limited counseling skills are the predominant barriers to physical activity counselling. Issues of internal validity (i.e., effectiveness and implementation) have received much more attention in the literature than have issues of external validity (i.e., reach and adoption).

Conclusions:

The research agenda for primary care-based physical activity interventions needs greater attention to the feasibility of adoption by busy primary care staff, generalizability, and dissemination.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 2 (2005): Issue 2 (Apr 2005)

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Measuring Physical Activity Change in Broad-Reach Intervention Trials

Marina M. Reeves, Alison L. Marshall, Neville Owen, Elisabeth A.H. Winkler, and Elizabeth G. Eakin

Background:

We compared the responsiveness to change (prepost intervention) of 3 commonly-used self-report measures of physical activity.

Methods:

In a cluster-randomized trial of a telephone-delivered intervention with primary care patients, physical activity was assessed at baseline and 4 months (n = 381) using the 31-item CHAMPS questionnaire; the 6-item Active Australia Questionnaire (AAQ); and, 2 walking for exercise items from the US National Health Interview Survey (USNHIS). Responsiveness to change was calculated for frequency (sessions/week) and duration (MET·minutes/week) of walking and moderate-to-vigorous intensity physical activity.

Results:

The greatest responsiveness for walking frequency was found with the USNHIS (0.45, 95% CI: 0.19, 0.72) and AAQ (0.43, 95% CI: 0.19, 0.67), and for walking duration with the USNHIS (0.27, 95%CI 0.13, 0.41) and CHAMPS (0.24, 95% CI: 0.12, 0.36). For moderate-to-vigorous activity, responsiveness for frequency was slightly higher for the AAQ (0.50, 95% CI: 0.30, 0.69); for duration it was slightly higher for CHAMPS (0.32, 95% CI: 0.17, 0.47).

Conclusions:

In broad-reach trials, brief self-report measures (USNHIS and AAQ) are useful for their comparability to population physical activity estimates and low respondent burden. These measures can be used without a loss in responsiveness to change relative to a more detailed self-report measure (CHAMPS).

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 7 (2010): Issue 2 (Mar 2010)

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Responsiveness to Change of Self-Report and Device-Based Physical Activity Measures in the Living Well With Diabetes Trial

Winnie Y.H. Lee, Bronwyn K. Clark, Elisabeth Winkler, Elizabeth G. Eakin, and Marina M. Reeves

Background:

This study evaluated the responsiveness to change in physical activity of 2 self-report measures and an accelerometer in the context of a weight loss intervention trial.

Methods:

302 participants (aged 20 to 75 years) with type 2 diabetes were randomized into telephone counseling (n = 151) or usual care (n = 151) groups. Physical activity (minutes/week) was assessed at baseline and 6-months using the Active Australia Survey (AAS), the United States National Health Interview Survey (USNHIS) walking for exercise items, and accelerometer (Actigraph GT1M; ≥1952 counts/minute). Responsiveness to change was calculated as responsiveness index (RI), Cohen’s d (postscores) and Cohen’s d (change-scores).

Results:

All instruments showed significant improvement in the intervention group (P < .001) and no significant change for usual care (P > .05). Accelerometer consistently ranked as the most responsive instrument while the least responsive was the USHNIS (responsiveness index) or AAS (Cohen’s d). RIs for AAS, USNHIS and accelerometer did not differ significantly and were, respectively: 0.45 (95% CI: 0.26–0.65); 0.38 (95% CI: 0.20–0.56); and, 0.49 (95% CI: 0.23–0.74).

Conclusions:

Accelerometer tended to have the highest responsiveness but differences were small and not statistically significant. Consideration of factors, such as validity, feasibility and cost, in addition to responsiveness, is important for instrument selection in future trials

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 12 (2015): Issue 8 (Aug 2015)