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Emma E. Sypes, Genevieve Newton and Zakkoyya H. Lewis

response, research has focused on designing and testing interventions that aim to improve participants’ PA levels and promote weight loss. In recent studies, electronic activity monitor systems (EAMSs) have been incorporated as a PA self-monitoring component of these interventions. An EAMS is defined as

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Michael B. Martin and Mark H. Anshel

Two experiments were conducted to examine the effect of self-monitoring (SM) strategies on motor performance of varied difficulty. In a pilot test, participants’ perceptions of task difficulty agreed with performance on the easy task. Participants perceived the hard task to be significantly more difficult than indicated by the performance scores and perceived the easy task to be significantly less difficult than their performance on the complex task (p < .05). In the subsequent experiment, subjects performed 90 trials on either the difficult or easy motor task using either positive self-monitoring (PSM), negative self-monitoring (NSM), or no self-monitoring. MANOVAs indicated that PSM resulted in superior performance in comparison to NSM across trials while performing the difficult task (p < .05). In the easy task, PSM was inferior to NSM on motor performance across trials (p < .01). Further results also indicated that negative affect significantly decreased for PSM performing the difficult task, and for NSM performing the easy task.

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K. Michelle Hume, Garry L. Martin, Patricia Gonzalez, Clayton Cracklen and Sheldon Genthon

Behavioral coaching techniques consisting of instructions, a self-monitoring checklist, and coach feedback were examined at freestyle practice sessions with three female prenovice figure skaters. These techniques were compared to normal coaching procedures for their effects on the frequency of jumps and spins performed, the number of times a skater practiced a routine to music, and the amount of time spent engaging in off-task behaviors during 45-min free-skating sessions. Within a reversal-replication design, the behavioral coaching techniques produced considerable improvement on all dependent measures. Social validation measures indicated that the procedures improved quality of skating and were rated positively by the coach and by two of the three skaters.

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Jo Weber and Eleanor H. Wertheim

Upon becoming members at a community gymnasium, 55 women were randomly assigned to one of three groups: control, self-monitoring of gym attendance, or self-monitoring of attendance plus extra staff attention. The effect of these interventions on gym attendance over 3 months was examined. A 3 X 4 (Group X Time Phase, first 3 weeks to last 3 weeks) ANOVA indicated that the main effects for group and time predicted attendance at the gym. Attendance during the first 3 weeks was significantly greater than attendance thereafter. The control subjects attended significantly less than the self-monitoring subjects at all phases. Further research is suggested toward using self-monitoring, staff support, and periodic progress feedback for increasing program adherence. In addition, self-motivation and body fat percent were assessed initially. Correlations between these two variables and attendance failed to support their usefulness as predictors at any time phase.

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Margaret P. Lott, Andrea Kriska, Emma Barinas-Mitchell, Li Wang, Kristi Storti, Daniel G. Winger and Molly B. Conroy

Background:

Lifestyle interventions promote increased physical activity (PA) and weight loss; however, relapse to sedentary behavior and weight regain are common.

Methods:

We analyzed baseline and 24-month data from participants in the Slow the Adverse Vascular Effects (SAVE) study. SAVE included an 18-month behavioral intervention. At 24 months, participants completed a survey about lifestyle strategies used in past 6 months. PA levels were assessed with the Modifiable Activity Questionnaire. We compared change in weight, BMI, and PA from baseline to 24 months by use of strategies vs. no use.

Results:

214 participants (61%) completed 24-month visit. 74% were female and 86% were white. At 24 months, 65% used self-monitoring, 67% group/commercial support, 94% other behavioral skills, and 27% used professional support within past 6 months. At 24 months, participants who used self-monitoring (5.2 vs. –0.8 MET-hr/wk; P = .001) and group/commercial support (4.3 vs. 0 MET-hrs/wk; P = .01) had greater PA increases compared with those who did not use strategies. Participants who used other behavioral strategies had a significantly greater percent decrease in weight than those who did not.

Conclusions:

Of the lifestyle strategies used following intervention, self-monitoring and group/commercial support may be particularly important in longer-term PA levels.

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Dorothy Pekmezi, Shira Dunsiger, Ronnesia Gaskins, Brooke Barbera, Becky Marquez, Charles Neighbors and Bess Marcus

Background:

Due to high rates of inactivity and related chronic illnesses among Latinas,1 the current study examined the feasibility and acceptability of using pedometers as an intervention tool in this underserved population.

Methods:

Data were taken from a larger randomized, controlled trial2 and focused on the subsample of participants (N = 43) who were randomly assigned to receive a physical activity intervention with pedometers and instructions to log pedometer use daily and mail completed logs back to the research center each month for 6 months.

Results:

Retention (90.7% at 6 months) and adherence to the pedometer protocol (68.89% returned ≥ 5 of the 6 monthly pedometer logs) were high. Overall, participants reported increased physical activity at 6 months and credited pedometer use for helping them achieve these gains (75.7%). Participants who completed a high proportion (≥ 5/6) of pedometer logs reported significantly greater increases in physical activity and related process variables (stages of change, self-efficacy, behavioral processes of change, social support from friends) than those who were less adherent (completed < 5 pedometer logs).

Conclusions:

Pedometers constitute a low-cost, useful tool for encouraging self-monitoring of physical activity behavior in this at-risk group.

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Johanna Nurmi, Martin S. Hagger, Ari Haukkala, Vera Araújo-Soares and Nelli Hankonen

This study tested the predictive validity of a multitheory process model in which the effect of autonomous motivation from self-determination theory on physical activity participation is mediated by the adoption of self-regulatory techniques based on control theory. Finnish adolescents (N = 411, aged 17–19) completed a prospective survey including validated measures of the predictors and physical activity, at baseline and after one month (N = 177). A subsample used an accelerometer to objectively measure physical activity and further validate the physical activity self-report assessment tool (n = 44). Autonomous motivation statistically significantly predicted action planning, coping planning, and self-monitoring. Coping planning and self-monitoringmediated the effect of autonomous motivation on physical activity, although self-monitoring was the most prominent. Controlled motivation had no effect on self-regulation techniques or physical activity. Developing interventions that support autonomous motivation for physical activity may foster increased engagement in self-regulation techniques and positively affect physical activity behavior.

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Kristiann Heesch, Louise C. Mâsse, Ralph F. Frankowski and Andrea L. Dunn

Background:

Interventions that teach strategies for integrating physical activity into a person’s daily routine are becoming more common. These interventions have been found to increase physical activity behavior, although the increases have not been large. The small to moderate changes in physical activity can result from participants having insufficient adherence to the intervention protocol to produce an intervention effect. Given that adherence is likely to affect the power to find a treatment effect, it should be tracked. This study examined changes in adherence over 6 months for a lifestyle physical activity intervention.

Methods:

Participants were 244 sedentary adults who took part in the Project PRIME lifestyle physical activity intervention. Adherence was assessed separately for a group-based intervention (PRIME G) and a telephone- and mail-based intervention (PRIME C). Markers of adherence were completion of homework, self-monitoring of physical activity, attendance at class (PRIME G only), and completion of monthly telephone calls (PRIME C only). Changes over time in adherence markers and differences between intervention groups for homework completion and adherence to self-monitoring were modeled with generalized estimating equations (GEE).

Results:

The probability of attending class, completing the telephone calls, and completing the homework decreased significantly over 6 months. Participants only self-monitored an average of 5 to 6 days each calendar month. Participants in the group-based intervention were more likely than those in the telephone- and mail-delivered intervention to complete the homework throughout the study.

Conclusions:

The findings suggest that individuals are willing to adhere with a telephone call protocol over 6 months. They are less willing to complete homework and attend class over this same time period. Most are not willing to self-monitor their lifestyle physical activities more than a few days a month.

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Devajyoti Deka

during the years surrounding retirement, especially in regard to changes in physical activities (ΔPA), health (ΔH), and weight (ΔW). It further hypothesizes that ΔH during the years surrounding retirement is a function of ΔPA and ΔW, whereas ΔPA is a function of ΔW, self-efficacy, self-monitoring, and

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Stephanie G. Kerrigan, Evan M. Forman, Mitesh Patel, Dave Williams, Fengqing Zhang, Ross D. Crosby and Meghan L. Butryn

Manipulation Following the baseline week, participants were provided with account information to access their data and instructions on syncing the device (ie, how to turn on Bluetooth). Participants had access to the self-monitoring features of the activity sensor (ie, daily and weekly graphs of their activity