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Responders and Nonresponders to a Walking Intervention for Sedentary Women

Authors

  • Mary A. Nies,

    1. Mary A. Nies, RN, PhD, FAAN, FAAHB, Rho, Assistant Vice President of Health Sciences for Research in Community Health, Professor of Nursing and of Preventive Medicine; Yiyuan Sun, RN, DNSc, Delta Mu, Postdoctoral Associate, Health Sciences Center; both at the State University of New York at Stony Brook, Stony Brook, NY. This work was supported by the National Institutes of Health NINR grant NR03999 to the primary author. The authors gratefully acknowledge assistance with data analysis by Dr. Ty Partridge, Assistant Professor of Psychology, Wayne State University. Correspondence to Dr. Nies, Health Sciences Center, State University of New York at Stony Brook, Stony Brook, NY, 11794. E-mail: maryannnies@yahoo.com
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  • Yiyuan Sun

    1. Mary A. Nies, RN, PhD, FAAN, FAAHB, Rho, Assistant Vice President of Health Sciences for Research in Community Health, Professor of Nursing and of Preventive Medicine; Yiyuan Sun, RN, DNSc, Delta Mu, Postdoctoral Associate, Health Sciences Center; both at the State University of New York at Stony Brook, Stony Brook, NY. This work was supported by the National Institutes of Health NINR grant NR03999 to the primary author. The authors gratefully acknowledge assistance with data analysis by Dr. Ty Partridge, Assistant Professor of Psychology, Wayne State University. Correspondence to Dr. Nies, Health Sciences Center, State University of New York at Stony Brook, Stony Brook, NY, 11794. E-mail: maryannnies@yahoo.com
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Abstract

Purpose: Increasing evidence has indicated that people might be differentially influenced by intervention programs. The purpose of this secondary analysis was to identify groups of women who responded differently to a walking intervention. Data used in this secondary analysis were collected in a longitudinal study of a counseling intervention to increase walking among 248 initially sedentary women.

Method: A latent growth-mixture modeling approach was used to assess treatment effects on growth in physical activity and mood over time. Subgroups of participants who were responsive versus those who were nonresponsive to intervention were also identified. Logistic-regression analysis was conducted to confirm group membership and identify predictors associated with the identified subgroups.

Results: Two subgroups (responders, nonresponders) were identified separately for physical activity and mood. Using several variables as predictors of group memberships, 92%–95% of the cases were correctly classified. The current study indicated that predictors for group membership were associated with the outcome variables.

Conclusions: These findings indicated that optimal interventions should be tailored to not only the physical, psychosocial, and environmental variables of each woman, but also to outcome variables of interest to the woman.

Clinical Relevance: Nurses practicing in community and public health settings should determine physical activity interventions that are based on scientific findings and on outcomes that are important for the individual woman.

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