To examine the impact of body mass index (BMI) on the effectiveness of a disease management-health promotion intervention among community-dwelling Medicare beneficiaries with disabilities.
Secondary data analyses of a randomized controlled trial.
Nineteen counties in upstate New York and on the West Virginia-Ohio border.
Four hundred fifty-two Medicare beneficiaries who participated in the Medicare Primary and Consumer-Directed Care Demonstration between August 1998 and June 2002 and completed the 22-month follow-up.
Multicomponent disease management-health promotion intervention involving patient education, individualized health promotion coaching, medication management, and physician care management.
Body mass index and dependence in Activities of Daily Living (ADLs).
Multivariate linear regression.
The intervention resulted in significantly less worsening in ADLs dependence among normal-weight participants (coefficient, -.42; p = .04). However, the intervention did not have a significant effect for underweight participants (F test p = .33 vs. underweight participants in the control group) or overweight or obese participants (F test p = .78 vs. overweight or obese participants in the control group).
A positive effect of the intervention on disability was found among normal-weight participants but not among underweight or overweight or obese participants. Future health promotion interventions should take into consideration the influence of BMI categories on treatment effects.
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"The lack of statistical significance is largely due to relatively small sample sizes because the original demonstration was not designed with this subgroup analysis in mind. Nevertheless, these results, combined with other findings that show the effects of health promotion interventions on functional outcomes for individuals in different weight categories, suggest that intervention strategies might need to be tailored to address specific BMI levels of participants (Al et al., 2007; Meng et al., 2010). For example, additional therapies to enhance weight loss such as pharmacologic methods and surgery might be considered among individuals who are obese (Picot et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: To examine the effect of body mass index (BMI) on the impact of a health promotion intervention on health services use and expenditures among Medicare beneficiaries with disabilities.
We analyzed data from 452 Medicare beneficiaries who participated in a Medicare demonstration. The intervention included the following components: patient education, health promotion coaching, medication management, and physician care management. We performed the analysis by using generalized linear models (GLM) to examine the impact of BMI and the intervention on total health care expenditures.
The intervention was cost neutral over the 2-year study period. Participants in the intervention group used less home health aide services (p = .03) and had fewer nursing home days (p = .05). The intervention appeared to have smaller effects on expenditures as BMI level increased.
The findings suggest that a health promotion intervention may achieve better beneficiary outcomes without an increase in resource use in this Medicare population.
Journal of Aging and Health 02/2011; 23(4):743-63. DOI:10.1177/0898264310395755 · 1.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose:
The objectives of this study were to investigate the psychometric properties of the SF-36 in a sample of older adults with chronic conditions and to test whether measurement bias exists based on the levels of comorbidity.
Participants included were 979 cognitively intact older adults with comorbidities who were interviewed at their homes. We examined the psychometric properties of the SF-36 and conducted confirmatory factor analysis (CFA) to investigate the assumption of measurement invariance by the levels of comorbidity.
Overall data quality was high and scaling assumptions were generally met with few exceptions. Floor and ceiling effects were present for the role-physical and role-emotional subscales. Using CFA, we found that a three-factor measurement model fits the data well. We identified two violations of measurement invariance. Results showed that participants with high comorbidity level place more emphasis on social functioning (SF) and bodily pain (BP) in relation to physical health-related quality of life (HRQoL) than those with low comorbidity level.
Measurement bias was present for the SF and BP components of the SF-36 physical HRQoL measure. Researchers should be cautious when considering the use of SF-36 in clinical studies among older adults with comorbidities.
Quality of Life Research 03/2013; 22(9). DOI:10.1007/s11136-013-0373-1 · 2.49 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Abstract Objective . To systematically review health coaching interventions regarding effectiveness of health coaching for specific outcomes, optimal intervention approaches, and identification of specific techniques associated with effectiveness. Data Source . Articles were sourced from CINAHL, Global Health, PsycINFO, Academic Search Complete, Health Source, Psychology and Behavioral Sciences Collection, and Medline. Study Inclusion and Exclusion Criteria . Randomized controlled trials were included if the study (1) employed health coaching according to a predefined criterion; (2) clearly reported the use of health coaching; or (3) incorporated the use of coaching. Data Extraction . Aims, participants, approach, behavior change techniques (BCTs), and findings pertaining to each study were summarized. BCTs were classified according to the CALO-RE taxonomy. Data Synthesis . Data were synthesized by cross-tabulation of BCTs with study outcomes. Results . Fifteen of 16 eligible studies reported a positive intervention effect in at least one outcome. Nine studies (56%) did not define health coaching; the number of intervention sessions provided ranged from 2 to 48; and in three studies, one or more intervention details were unclear. It was hence difficult to synthesize the studies to adequately address our research questions. Conclusion . Health coaching is a promising strategy for health improvements; however, future research should ensure clarity in reporting intervention details, clearer definitions of health coaching/theoretical bases, consistency in reporting BCTs, and the inclusion of process variables as outcome measures.
American journal of health promotion: AJHP 04/2014; 29(5). DOI:10.4278/ajhp.130510-LIT-238 · 2.37 Impact Factor