Research ArticleOriginal ResearchA

Effect of Preventive Messages Tailored to Family History on Health Behaviors: The Family Healthware Impact Trial

Mack T. Ruffin, Donald E. Nease, Ananda Sen, Wilson D. Pace, Catharine Wang, Louise S. Acheson, Wendy S. Rubinstein, Suzanne O’Neill, Robert Gramling and ; for The Family History Impact Trial (fhitr) Group
The Annals of Family Medicine January 2011, 9 (1) 3-11; DOI: https://doi.org/10.1370/afm.1197

Article Figures & Data

Figures

Tables

  • Table 1.

    Demographics of Study Participants by Study Arm at Baseline

    CharacteristicsIntervention Arm (n=2,364) No. (%)Control Arm (n=1,422) No. (%)
    Note: After adjusting for practice clustering and site differences, the only statistically significant difference between the study arms exists for starting season (P=.003).
    a Income for 12% was not reported in either group.
    Sex, female1,676 (71)962 (68)
    Age, mean (SD), y50.3 (8.4)51.1 (8.0)
    Hispanic or Latino58 (2)29 (2)
    Race
        White2,134 (90)1,320 (93)
        Black or African American87 (4)35 (3)
        Asian70 (3)31 (2)
        Other42 (2)20 (1)
        More than 1 race31 (1)16 (1)
    Marital status
        Single, never married203 (9)96 (7)
        Married or living with partner1,857 (79)1,135 (80)
        Separated or divorced260 (11)160 (12)
        Widowed44 (2)31 (2)
    Season study started
        January–April1,179 (50)411 (29)
        May–August704 (30)375 (26)
        September–December481 (20)636 (45)
    Annual household incomea
        Less than $25,00091 (4)41 (3)
        $25,001 to $35,000102 (5)45 (4)
        $35,001 to $50,000218 (11)106 (8)
        $50,001 to $75,000402 (19)228 (18)
        More than $75,0001,262 (61)834 (66)
    Currently has health insurance2,276 (96)1,380 (97)
  • Table 2.

    Risk Levels Based on Family History for the 6 Diseases by Study Arm

    Intervention Arm (n = 2,330)aNo. (%)Control Arm (n = 1,255)aNo. (%)
    RiskWeakModerateStrongWeakModerateStrong
    a Sample size excludes participants without complete family history data.
    b P value <.05 is based on a comparison of proportions between study arms and adjusted for practice clustering and site differences.
    Coronary heart disease947 (41)615 (26)768 (33)502 (40)323 (26)430 (34)
    Stroke1,212 (52)783 (34)335 (14)640 (51)419 (33)196 (16)
    Diabetesb1,426 (61)643 (28)261 (11)812 (65)302 (24)141 (11)
    Colorectal cancer2,015 (87)263 (11)52 (2)1069 (85)147 (12)39 (3)
    Breast cancer1,799 (77)305 (13)226 (10)990 (79)139 (11)126 (10)
    Ovarian cancer2,107 (91)125 (5)98 (4)1135 (90)73 (6)47 (4)
  • Table 3.

    Lifestyle and Screening Behaviors of Participants at Baseline by Study Arm

    CharacteristicsIntervention Arm (n=2,364) No. (%)Control Arm (n=1,422) No. (%)
    Note: Comparison between study arms was carried out after adjusting for practice clustering and covarying effects of age, sex and study sites.
    a P<.05.
    Smoking
        Current185 (8)108 (8)
        Former701 (30)415 (29)
        Never1,478 (62)899 (63)
    Fruit and vegetable intake
        <2 servings a day430 (18)251 (18)
        2–4 servings a day1,546 (65)939 (66)
        ≥5 servings a day388 (17)160 (16)
    Physical activity
        None to less than 10 min per week82 (4)56 (4)
        1–4 times a week >10 min to <30 min each event1,587 (69)928 (67)
        5–6 times a week at least for 30 min each event620 (27)397 (29)
    Aspirin use (<3 d/wk)a1,608 (83)851 (73)
    Blood pressure measured >1 y ago191 (8)99 (7)
    Cholesterol level measured >5 y ago156 (7)73 (5)
    Blood glucose level measured >2 y ago794 (34)427 (30)
  • Table 4.

    Behavioral Change Subgroups for Each Outcome by Study Arm

    Behavior Change GroupsaIntervention ArmbNo. (%)Control Arm No. (%)b
    a The maintained category means the participants had the same behavior identified at baseline and month 6 based upon self-report. The increased category means the participants were not at goal at baseline and reported at goal for the specific behavior at month 6.
    b The sample size for each behavior does not equal the total study sample for each arm because of missing or incomplete data.
    c Subset of the study population used to determine whether the intervention was significantly more effective in moving participants to at goal compared with the control intervention.
    d For blood pressure measurement, the goal was to have had a blood pressure reading by physician within the last year. The category “Still no measurement within past year” reported no blood pressure measurement within a year of baseline and at month 6. The category “Measurement obtained” represents participants with no blood pressure measurement within a year at baseline but who reported a blood pressure measurement within a year at month 6.
    e For cholesterol, the goal was a cholesterol level measurement within the last 5 years. The category “Still no measurement in past 5 y” means no cholesterol level was measured within 5 years from baseline to month 6. The category “Measurement obtained” represents participants with no cholesterol level measurement within 5 years at baseline but who reported cholesterol level measurement within 5 years at month 6.
    f For blood glucose, the goal was a blood glucose level measurement within the last 2 years. The category “Still no measurement in past 2 y” means no blood glucose level measured within 2 years of baseline and month 6. The category “Measurement obtained” represents participants with no blood glucose level measurements within 2 years at baseline but who reported a blood glucose level measurement within 2 years at month 6.
    Smoking2,1101,278
        Maintained smokingc129 (6)78 (6)
        Quit smokingc26 (2)17 (2)
        Maintained never or former1,944 (92)1,177 (92)
        Started smoking11 (1)6 (1)
    Fruit and vegetable intake2,1111,278
        Maintained <5 servings a dayc1,560 (74)973 (76)
        Increased to >5 servings a dayc193 (9)89 (7)
        Maintained ≥5 servings a day261 (12)159 (12)
        Moved to <5 servings a day97 (5)57 (5)
    Physical activity2,0331,236
        Maintained physical activity <5–6 times a week for <30 min each eventc1,249 (62)782 (63)
        Increased physical activity 5–6 times a week for ≥30 min each eventc218 (11)99 (8)
        Maintained 5–6 times a week for ≥30 min each event412 (20)240 (20)
        Decreased physical activity <5–6 times a week for <30 min each event152 (7)114 (9)
    Aspirin use1,9591,159
        Maintained <3 d/wkc1,458 (74)775 (67)
        Increased to ≥3 d/wkc150 (8)76 (7)
        Maintained ≥3 d/wk307 (16)251 (22)
        Decreased to <3 d/wk44 (2)57 (5)
    Blood pressure measuredd2,1101,277
        Still no measurement within past yearc16 (1)7 (1)
        Measurement obtainedc147 (7)78 (6)
        Measurement within 1 y for entire study1,896 (90)1,155 (91)
        Measurement no longer within 1 y51 (2)37 (3)
    Cholesterol level measurede2,0251,203
        Still no measurement in past 5 yc42 (2)13 (1)
        Measurement obtainedc51 (2)31 (3)
        Measurement within 5 y for entire study1,857 (92)1,124 (93)
        Measurement lapsed75 (4)35 (3)
    Blood glucose level measuredf1,7261,034
        Still no measurement in past 2 yc103 (6)54 (5)
        Measurement obtainedc120 (7)51 (5)
        Measurement within 2 y for entire study1,466 (85)906 (88)
        Measurement lapsed37 (2)23 (2)
  • Table 5.

    Contrast of Movement From Not at Lifestyle Goal to at Goal Compared With Persistently Not at Goal for Each Behavior by Study Arm

    VariableOR (95% CI)a
    CI=confidence interval; OR = odds ratio.
    a Odds ratio exhibits odds of the intervention group moving in a positive direction with reference to the control group. Logistic regression model is adjusted for practice clustering and site, baseline body mass index, sex, baseline smoking status (except for quit smoking variable), frequency of moderate/strong family risks, and risk perception score for the 6 diseases. Diet and physical activity variables were further adjusted for season in which questionnaires were filled out.
    b Model run without site adjustment.
    Quit smoking1.18 (0.47–2.95)
    Increased to ≥5 serving of fruit and vegetables each day1.29 (1.05–1.58)
    Increased physical activity to 5–6 times a week for ≥30 min each event1.47 (1.08–1.98)
    Aspirin use increased to ≥3 d/wk0.91 (0.64–1.29)
    Blood pressure measured by health care professional within the last yearb1.44 (0.29–7.16)
    Blood cholesterol level measured within 5 y0.34 (0.17–0.67)
    Blood glucose level measured within 2 y1.08 (0.61–1.91)

Additional Files

  • Supplemental Appendixes

    Supplemental Appendix 1. Family Healthware Health Messages and Recommendations;Supplemental Appendix 2. Standard Health Message. Family Healthware Study ControlHealth Messages and Recommendations

    Files in this Data Supplement:

  • The Article in Brief

    Effect of Preventive Messages Tailored to Family History on Health Behaviors: The Family Healthware Impact Trial

    Mack T. Ruffin, IV , and colleagues

    Background A new online tool helps primary care practices collect and use family history to help prevent disease. This study looks at whether patients who receive a risk assessment and messages tailored to their family health history of six diseases are more likely to change their lifestyle behaviors or get health screening, compared with patients who receive a generic preventive health message.

    What This Study Found Using a Web-based tool to screen for family history and tailor prevention messages to family risk improves some health behaviors. Preventive messages tailored to family risk for coronary heart disease, stroke, diabetes, and colorectal, breast, and ovarian cancers modestly increases fruit and vegetable consumption and physical activity. Specifically, intervention participants were 3 percent more likely to increase daily fruit and vegetable consumption from 5 or fewer servings a day to 5 or more servings a day and 4 percent more likely to increase physical activity to five to six times a week for 30 minutes or more compared with patients receiving a generic preventive health message. The untailored (generic) message increased the percentage of patients getting a cholesterol screening.

    Implications

    • Familial risk may be an important motivator of health behavior change.
    • Additional research is needed to determine how to effectively implement family history assessment in primary care.

  • Annals Journal Club:

    Jan/Feb 2011

    Symbiosis Instead of Competing Demands: A Tale of 2 Preventive Services

    The Annals of Family Medicine encourages readers to develop a learning community of those seeking to improve health care and health through enhanced primary care. You can participate by conducting a RADICAL journal club and sharing the results of your discussions in the Annals online discussion for the featured articles. RADICAL is an acronym for Read, Ask, Discuss, Inquire, Collaborate, Act, and Learn. The word radical also indicates the need to engage diverse participants in thinking critically about important issues affecting primary care and then acting on those discussions.1

    How it Works

    In each issue, the Annals selects an article or articles and provides discussion tips and questions. We encourage you to take a RADICAL approach to these materials and to post a summary of your conversation in our online discussion. (Open the article online and click on "TRACK Comments: Submit a response.") You can find discussion questions and more information online at: http://www.AnnFamMed.org/AJC/.

    CURRENT SELECTION

    Article for Discussion

    • Ruffin MT IV, Nease DE Jr, Sen A, et al. Effect of preventive messages tailored to family history on health behaviors: the Family Healthware Impact Trial. Ann Fam Med. 2011;9(1):3-11.

    Discussion Tips

    This cluster-randomized clinical trial assesses the effect of an automated family medical history assessment and tailored messages on preventive behaviors compared with a standard preventive message. In addition to critiquing the study, consider its larger implications for the family focus of family practice and the emerging genetic revolution.

    Discussion Questions

    • What question is addressed by the article? How does the question fit with what already is known on this topic?
    • How does a conceptual model inform the intervention design and your interpretation of the results?
    • How strong is the study design for answering the question?
    • How do the study methods compare with the CONSORT criteria for clinical trials?2
    • What is the degree to which can the findings be accounted for by:
    1. The choice of the comparison intervention?
    2. How participants (settings, practices, clinicians, and patients) were selected? The exclusion criteria and dropouts? Are any biases likely to be important?
    3. How outcomes were measured?
    4. Confounding (false attribution of causality because 2 variables discovered to be associated actually are associated with a 3rd factor)?
    5. How information was interpreted?
    6. Chance?
  • What are the main findings? How large is the effect across different outcomes?
  • How transportable are the findings to your clinical setting? What factors might affect this transportability?
  • Could you apply these findings to your practice using MyFamilyHealth Portrait, at https://familyhistory.hhs.gov/fhh-web/home.action?
  • What are the implications of the current limited utility of most electronic health records (EHRs) for gathering, synthesizing, analyzing and using family history information? Is the growing use of EHRs affecting the family focus of family practice? What is the potential for EHRs to support a family focus to care? How might this potential be realized? How could EHRs support primary care clinicians, patients, and families in managing the forthcoming onslaught of genetic/genomic information?
  • What are some next steps for applying the findings or answering other questions that this study raises?

    • References

    1. Stange KC, Miller WL, McLellan LA, et al. Annals journal club: It�s time to get RADICAL. Ann Fam Med. 2006;4(3):196-197. http://annfammed.org/cgi/content/full/4/3/196.
    2. CONSORT Group. CONSORT: Consolidated Standards of Reporting Trials. http://www.consort-statement.org/. Accessed Dec 23, 2010.
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Get Permissions

Jump to section

Related Articles

Cited By...

More in this TOC Section

Similar Articles

Subjects