Abstract
Background and Objectives
Evidence of effective multifactorial lifestyle interventions for primary stroke prevention is lacking, despite the significant contribution of lifestyle to stroke burden. We aimed to determine the efficacy of health and wellness coaching (HWC) for primary stroke and cardiovascular disease (CVD) prevention in adults at a moderate-to-high CVD risk.
Methods
This was a parallel, 2-arm, open-label, single-blinded, phase III randomized controlled trial to determine the efficacy of HWC for primary stroke prevention in individuals 30 years and older with a 5-year CVD risk ≥10% as measured by 5-year absolute CVD risk (as measured by the PREDICT tool) at 9 months post-randomization. Eligible participants were those with a 5-year CVD risk ≥10%, with no history of stroke, transient ischemic attack, or myocardial infarction. The relative risk reduction (RRR) and odds ratios (OR) were evaluated separately in those at moderate (10%–14%) 5-year CVD risk and those at high risk (≥15%) at baseline. The Life's Simple 7 (LS7) score for lifestyle-related CVD risk, as the indicator of cardiovascular health, was a key secondary outcome.
Results
Of a total of 320 participants, 161 were randomized to the HWC group and 159 to the usual care (UC) group. HWC resulted in a statistically significant RRR of -10.9 (95% CI −21.0 to −0.9) in 5-year CVD risk in the higher CVD risk group but no change in the moderate risk group. An improvement in the total LS7 score was seen in the HWC group compared with the UC group (absolute difference = 0.485, 95% CI [0.073 to 0.897], p = 0.02). Improvement in blood pressure scores was statistically significantly greater in the HWC group than in the UC group for those at high risk of CVD (OR 2.28 [95% CI 1.12 to 4.63] and 1.55 [0.80 to 3.01], respectively). No statistically significant differences in mood scores, medication adherence, quality of life, and satisfaction with life scores over time or between groups were seen.
Discussion
Health and wellness coaching resulted in a significant RRR in the 5-year CVD risk compared with UC at 9 months post-randomization in patients with a high baseline CVD risk. There was no improvement in CVD risk in the moderate risk group; hence, this study did not meet the primary hypothesis. However, this treatment effect is clinically significant (number needed to treat was 43). The findings suggest that HWC has potential if further refined to improve lifestyle risk factors of stroke.
Introduction
The presence of ideal healthy lifestyle factors such as healthy diet, not smoking, adequate physical activity, normal body weight, and low alcohol consumption has been shown to substantially reduce risk of stroke, coronary artery disease, myocardial infarction, and overall cardiovascular disease (CVD) mortality.1-4 Behavioral interventions and health coaching aimed at improving lifestyle behavior have been suggested for CVD prevention.5,6 Health and wellness coaching (HWC) is a health-focused client-centric intervention based on the principles of positive psychology7 that aims to enhance health and well-being and improve lifestyle behaviors.8-11 Previous studies have shown that HWC has produced positive effects on health and improved well-being of patients with chronic disease,10,12,13 but there were no previous trials of HWC for primary stroke and/or cardiovascular disease (CVD) prevention.
This study aimed to determine the efficacy of HWC for primary relative risk reduction (RRR) in adults with a moderate-to-high risk of CVD, as determined by CVD risk screening in primary care.
Methods
Trial Design Overview
We conducted a parallel, 2-arm, open-label, single-blinded randomized controlled trial, based on the PROBE design,14 to determine the efficacy of HWC on primary stroke/CVD prevention in moderate-to-high CVD risk adults, as determined by absolute 5-year CVD risk.15 The detailed methods of the trial have been described previously.16 The trial was approved by the National Health and Disability Ethics Committee (ref: 16/NTA/36) and the Auckland University of Technology Ethics Committee (ref: 16/174) and registered with the Australian New Zealand Clinical Trial Registry (ACTRN N12616000379415) on 23/03/2016. Only individuals who met inclusion criteria, did not have exclusion criteria and gave written informed consent to participate in the study were randomized. The study participants were enrolled over the two-year period after trial registration (2016–2018).
The primary hypothesis was that a 15-session HWC intervention conducted over a 9-month period would result in a clinically significant 10% RRR among those at moderate 5-year CVD risk (10%–14%) and a 25% RRR among those at high risk (≥15%) with 5-year absolute risk of CVD ≥10% at baseline as compared with usual care. The secondary hypotheses were that HWC would result in (1) an improvement in the LS7 total cardiovascular risk score17 overall and on its individual lifestyle components (blood pressure, blood glucose, blood cholesterol, weight, physical activity, smoking status, and diet) at the 9-month follow-up; (2) self-reported improvement in adherence to medication; (3) reduced cardiovascular events (new stroke or coronary heart disease, both fatal or non-fatal); (4) improved health-related quality of life; (5) reduced scores for depression; and (6) an increase in participant life satisfaction at 3, 6, 9, and 12 months post-randomization. We also registered all new stroke, transient ischemic attack (TIA), and acute CVD events.
Participants and Procedures
Potentially eligible participants were identified by clinicians through Auckland general practice clinics using preselection eligibility criteria (for details on the study protocol, see Supplementary materials, eSAP 1, links.lww.com/CPJ/A481). A list of potential participants who consented to be contacted by the research team was provided by medical practice staff to the study manager. Participants were randomized between May 25, 2016, and July 9, 2019. Eligibility criteria included individuals with a five-year CVD risk ≥10% as determined by a risk assessment by their family doctor using the online PREDICT CVD risk assessment tool,18,19 those who were 30 years and older, those who had no history of stroke or myocardial infarction, and those who were able to provide written informed consent. Participants were not eligible if they were unable to converse in English, had significant comorbid conditions deeming them unsuitable for the trial as determined by their doctor, or had moderate-to-severe depression or anxiety as determined by the Patient Health Questionnaire (PHQ-9) cutoff score (>18).20
Of the 1,116 potentially eligible people, 767 were excluded from the study. Of them, 160 (21) did not meet the inclusion criteria, 255 (33%) declined to participate, 381 (50%) were not able to be contacted, and 29 (4%) had exclusion criteria (Figure). The remaining 320 participants (28.7%) were randomly assigned using an online randomization tool in the Active Research Technology (ART) database system21 to either the HWC group (n = 161) or UC group (n = 159). Age, sex, ethnicity, level of education, marital status, employment and living status, and medical conditions were identified from questionnaires and medical records (Table 1). A CONSORT flowchart of how participants moved through the trial is presented in the Figure. There was no statistically significant difference in the study participant baseline characteristics between those “lost to follow-up” in the HWC group (22%; n = 35) and control group (11%; n = 17).
Figure. CONSORT Study Flowchart.
Table 1.
Demographics of the Study Participants
| Characteristics | Health and wellness coaching (n = 159) | Usual care (n = 161) |
| Age (mean, SD) | 60.8 (9.5) | 60.8 (8.6) |
| Older than 35 years (n, %) | 159 (100.0) | 159 (98.8) |
| Sex: Male, n (%) | 98 (61.6) | 99 (61.5) |
| Ethnicity (n, %) | ||
| Māori | 34 (21.1) | 34 (21.1) |
| Pacific | 36 (23.0) | 37 (23.0) |
| Asian | 29 (18.2) | 28 (17.4) |
| NZ European/other | 60 (37.7) | 62 (38.5) |
| Marital status: Married, n (%) | 117 (73.6) | 107 (66.5) |
| Education: University, n (%) | 49 (30.8) | 49 (30.4) |
| Employment: aEmployed, n (%) | 112 (70.4) | 107 (66.5) |
| Living, n (%) | ||
| With partner/family | 137 (86.2) | 131 (81.4) |
| Alone | 15 (9.4) | 20 (12.4) |
| With others | 7 (4.4) | 10 (6.2) |
| Dwelling, n (%) | ||
| Other | 5 (3.1) | 15 (9.3) |
| Own home | 106 (66.7) | 92 (57.1) |
| Renting | 44 (27.7) | 49 (30.4) |
| Rest/nursing home, boarding house | 2 (1.3) | 4 (2.5) |
| Retirement village | 2 (1.3) | 2 (1.3) |
| Medical conditions, n (%) | ||
| Hyperlipidemia | 56 (35.2) | 50 (31.0) |
| High BP (≥140/90 mm Hg) | 57 (35.8) | 51 (31.6) |
| Diabetes mellitus | 47 (29.6) | 35 (21.7) |
| Heart disease (except myocardial infarction) | 7 (4.4) | 3 (1.9) |
| Peripheral vascular disease | 1 (0.6) | 0 (0) |
| Epilepsy | 1 (0.6) | 1 (0.6) |
| Traumatic brain injury | 1 (0.6) | 1 (0.6) |
| Other medical conditions | 15 (9.4) | 17 (10.6) |
Includes retired, unemployed, volunteer, and sickness beneficiary.
Participants were grouped into 4 equal strata according to the 4 main ethnic groups in New Zealand (NZ): Māori, Pacific, Asian, and NZ Europeans/others. Computerized online stratified minimization randomization was used to balance ethnic groups for possible prognostic factors: age (30–55 years, ≥56 years), sex, and 5-year CVD risk (10%–14% [moderate risk] and ≥15% [high risk]). Each participant was informed of their allocation and asked not to disclose this to their assessor.
Coaches attended an intensive six-week coaching course facilitated by a certified health and wellness coach. Training included core coaching competencies and code of ethics, developed by the International Coach Federation (ICF). ICF coaching is an internationally recognized approach effectively used in various settings. Coaches received regular group supervision, facilitated by the trainer, using a small-group approach.16 The intervention combined educational material and intensive HWC (eTable 1, links.lww.com/CPJ/A480). The details of the intervention have been described using the TIDieR guidelines22 (eTable 2).
Those in the HWC group were assigned a coach and allocated 15 sessions with their coach. The first 2 sessions and the final session were conducted in-person and the remaining sessions over the telephone. The first 12 sessions were conducted every 2 weeks and then the remaining 3 were conducted monthly, with the coaching sessions taking place over the course of 9 months. Coaching sessions initially lasted up to 1 hour during the in-person sessions, with later telephone sessions lasting 30–40 minutes. Between the final coaching session and the 9-month assessment, HWC participants received a single short (<10 minute) telephone ‘booster’ call from their coach to encourage maintenance of behavior change. The outline and content of the coaching sessions are summarized in eTable 1 (links.lww.com/CPJ/A480). UC study participants received usual medical care at the discretion of treating clinicians. The type and quantity of the care received by all participants were recorded through questionnaires and documentation at each assessment. The UC group was contacted at the same frequency as the HWC group during the study for assessments. All participants were provided the NZ Heart Foundation booklet, available to the general public through their doctor or online, which included recommendations and guidelines on heathy lifestyle.23
Study Outcomes
The primary outcome was a clinically significant 10% RRR among those at moderate 5-year CVD risk (10%–14%) and a 25% RRR among those at high risk (≥15%) with 5-year absolute risk of CVD ≥10% at baseline as compared with usual care assessed using the PREDICT tool (using the licensed PREDICT software)18 at 9 months post-randomization. The key prespecified secondary outcomes were (1) the LS717 total score of CVD risk (total possible score of 14, with higher scores indicating better cardiovascular health) and scores on the individual lifestyle components contributing 0 for poor, 1 for intermediate, and 2 for good cardiovascular health (blood pressure, blood glucose, blood cholesterol, BMI, physical activity, smoking status, and diet) at the 9-month follow-up; (2) self-reported changes in adherence to medication24; (3) health-related quality of life as assessed using EQ5D25; (4) screening for depression using the PHQ920; (5) level of TIA/stroke awareness based on open-ended questions; and (6) life satisfaction26 at baseline, 3, 6, 9, and 12 months (eTable 3, links.lww.com/CPJ/A480). Secondary outcomes also included acute CVD events (new stroke, TIA, or myocardial infarction, both fatal and non-fatal) during the 12-month follow-up period. All outcome measures were collected in a standardized manner by the trained assessors blinded to the treatment allocation.
Statistical Considerations
Analysis was performed according to intention-to-treat principles. In the adjusted analyses, all tests of significance of hypotheses concerning treatment effect parameters were performed using a level of significance of 5% and two-sided alternatives. Covariates were included in the model for the purpose of avoiding chance bias, reducing variance in the study sample, and, in the case of logit models, to avoid bias (eTable 4, links.lww.com/CPJ/A480). Baseline values of an outcome were always included in the model as a covariate. The 5-year CVD risk score was analyzed using censored regression treating individuals with scores greater than 30% as right-censored. Changes in relative CVD risk due to randomization arms were computed as the ratio of the effect estimate and the mean baseline CVD risk. Numbers needed to treat or harm were computed as the reciprocal of the adjusted estimated absolute risk reduction. Odds ratios for LS7 scores were also calculated. 95% confidence intervals (CIs) for all treatment effects were estimated. Continuous outcomes were analyzed using linear regression, dichotomous outcomes using logistic regression, and ordered categorical outcomes using proportional odds regression. Participant-level random effects were used in the presence of repeated measures. The effect at a given time point on a repeatedly measured outcome was estimated from the full mixed-effects model. The time point was modeled as a factor in all analyses. A likelihood ratio test of heteroscedasticity across treatment arms was conducted after unblinding, and different variances modeled if homoscedasticity was rejected at the 5% level. No baseline covariates with more than 15% missingness were retained for adjustment. Covariates with 15% values missing or less were imputed using all available selectable baseline covariates to form a model. Missing outcome data were assumed missing at random. Under this assumption, longitudinal modeling when repeated measures are available yields unbiased effect estimates.27 When outcome data were only available at baseline and 9 months, we pooled the analyses over 15 multiply imputed data sets. Sensitivity analyses were conducted including adjusted analyses on complete-case data, unadjusted analyses on multiply imputed data using the allocated arm, and adjusted analyses on multiply imputed data with usual care used as effective allocation (for imputation only) when outcomes were missing.
The significance level was set at 0.05 against two-sided alternatives. We assumed 20% noncompliance and loss to follow-up28 and plausible values of the baseline means and standard deviations of the absolute 5-year CVD risk. With these assumptions, 27 participants per arm in the moderate (10%–15%) CVD risk group and 13 participants per arm in the high (≥15%) CVD risk group provided 80% power to detect clinically significant29,30 10% and 25% RRR31-34 in the absolute 5-year CVD risk between the HWC and UC arms 9 months after randomization in each group, respectively, without accounting for multiple testing: This was the targeted power in each ethnicity subgroup. Overall, 320 participants provided 90% power to detect approximate RRRs of 6% and 17% in the moderate and high-risk subgroups, respectively. Reporting of the trial results was done according to the CONSORT guidelines.35
Results
Of a total of 320 participants, 161 were randomized to the HWC group and 159 to the UC group. The demographics, baseline characteristics (Table 1), stratified mean CVD risks, and other variables (Tables 2 and 3) of the study participants in the HWC and UC groups were similar. NZ Europeans/other was the largest ethnic group (38%), and each of the 4 other ethnic group accounted for approximately 20% of participants. Most (83%) of the sample was in the moderate CVD risk category. Participants were older by 3 to 5 years in the high-risk group compared with the moderate-risk group. The overall mean (SD) CVD risk at baseline was 16.08 (6.31) in the HWC group and 16.04 (5.95) in the UC group.
Table 2.
Demographic Characteristics and Baseline CVD Risk Stratified by Randomization Arm and Baseline CVD Risk
| Baseline CVD risk | Health and wellness coaching | Usual care | ||
| Moderate CVD risk (n = 85) | High CVD risk (n = 74) | Moderate CVD risk (n = 85) | High CVD risk (n = 76) | |
| Mean age | 58.3 (8.4) | 62.6 (10.0) | 59.5 (7.3) | 60.7 (10.0) |
| Sex (male %) | 63.5 | 59.5 | 60.0 | 63.2 |
| Ethnicity (%) | ||||
| Māori | 22.4 | 20.3 | 21.2 | 21.1 |
| Pacific | 22.4 | 23.0 | 18.8 | 27.6 |
| Asian | 16.5 | 20.3 | 18.8 | 15.8 |
| European/other | 38.8 | 36.5 | 41.2 | 35.5 |
| CVD risk (%) baseline | 11.6 (1.2) | 21.2 (5.9) | 12.1 (1.3) | 20.5 (5.9) |
| Overall mean risk (%) | 16.08 (6.31) | 16.04 (5.95) | ||
Table 3.
Outcome Means and Standard Deviations by Treatment Arm at Baseline and 9 Months (Unadjusted)
| Outcome measure, means (SD) | Health and wellness coaching | Usual care | ||
| Baseline | 9-mo | Baseline | 9-mo | |
| 5-y CVD risk (%) | 16.08 (6.31) | 14.60 (6.69) | 16.04 (5.95) | 14.88 (6.75) |
| 5-y CVD risk median (Q1, Q3) | 14 (11, 18.5) | 13 (10, 17) | 14 (12, 17) | 14 (10, 19) |
| LS7 scores | ||||
| Total | 7.27 (2.12) | 7.42 (2.00) | 7.43 (2.22) | 7.12 (2.38) |
| Smoking | 1.65 (0.75) | 1.75 (0.64) | 1.57 (0.81) | 1.65 (0.74) |
| Body mass index | 0.62 (0.74) | 0.64 (0.74) | 0.67 (0.73) | 0.73 (0.78) |
| Physical activity | 1.04 (0.87) | 1.07 (0.76) | 1.09 (0.85) | 0.96 (0.83) |
| Health diet | 1.06 (0.64) | 1.06 (0.69) | 1.09 (0.67) | 1.09 (0.69) |
| Total cholesterol | 1.14 (0.61) | 1.27 (0.65) | 1.12 (0.66) | 1.11 (0.64) |
| Blood pressure | 0.75 (0.64) | 0.70 (0.68) | 0.72 (0.64) | 0.43 (0.60) |
| Fasting glucose | 1.01 (0.79) | 0.98 (0.82) | 1.17 (0.78) | 1.13 (0.82) |
| Alcohol use 3 mo (yes; %) | 56 (50) | 50 (50) | 55 (50) | 58 (50) |
| Alcohol frequency | ||||
| Once or more a day | 16 (16.7) | 6 (10.0) | 19 (18.4) | 15 (18.3) |
| Every 2–10 d | 43 (44.8) | 29 (48.3) | 48 (46.6) | 38 (46.3) |
| Once a fortnight or less | 37 (38.5) | 24 (40.0) | 36 (35.0) | 29 (35.4) |
| Don't know/refuse | 0 (0.0) | 1 (1.7) | 0 (0.0) | 0 (0.0) |
| Satisfaction with life (Satisfaction with Life Scale score) | 23.83 (6.33) | 25.61 (6.24) | 23.52 (6.12) | 26.22 (5.71) |
| EuroQol-5 dimensions | 75.42 (16.58) | 77.22 (13.88) | 76.50 (16.43) | 77.70 (15.48) |
| Patient Health Questionnaire (PHQ9) | 5.03 (4.69) | 3.44 (4.00) | 4.66 (5.18) | 2.87 (3.72) |
| Adherence (Morisky Medical Adherence Scale score) | 2.07 (1.49) | 1.84 (1.18) | 2.00 (1.28) | 1.86 (1.29) |
Primary Outcome
In the higher CVD risk group, there was a statistically significant adjusted reduction in the 5-year CVD risk of −2.33 percentage points (95% CI [−4.47 to −0.18], p = 0.034), equating to a 10.9% RRR (95% CI [−21.0 to −0.9]) in CVD risk in this group (Table 4). The corresponding number needed to treat was 43 (95% CI [3 to 82]). No statistically significant change (+0.65 percentage points, 95% CI [−1.37 to +2.66], p = 0.53) was seen in the moderate CVD risk group. The corresponding number needed to harm was 154 (95% CI [−324 to 632]). The 5-year CVD risk in the HWC group changed from 16.1% (6.3) at baseline to 14.6% (6.7) at 9 months while in UC, it changed from 16.0% (6.0) at baseline to 14.9% (6.8) at 9 months (complete cases, unadjusted results, Table 3). Subgroup analyses by ethnic groups showed no statistically significant difference between HWC and UC groups by ethnicity in 5-year CVD risk, or total LS7 score, except in the moderate-risk Asian group, in which there was a significant increase in the LS7 score in favor of the HWC intervention (1.78; 95% CI 0.24 to 3.33) (eTable 5, links.lww.com/CPJ/A480; additional details of the inferential analysis are present in the link provided in the eAppendix, p. 12).
Table 4.
Adjusted Regression Analysis of Primary and Secondary Outcomes at 9 Months Post-Randomization
| Outcome measures | Effect type | Overall | Baseline CVD risk 10%–14% | Baseline CVD risk ≥15% | ||||||
| Effect estimate | 95% CI | p Value | Effect estimate | 95% CI | p Value | Effect estimate | 95% CI | p Value | ||
| 5-y CVD risk (% points) | Differencea | −0.68 | −2.18 to +0.82 | 0.37 | 0.65 | −1.37 to 2.66 | 0.53 | −2.33 | −4.47 to −0.18 | 0.034 |
| Change in RRb | −4.2% | −13.6 to +5.1 | 5.5% | −11.5 to +22.5 | −10.9% | −21.0 to −0.9 | ||||
| LS7 components | ||||||||||
| Smoking status | Odds ratioc | 1.11 | 0.05 to 23.00 | 0.95 | 1.62 | 0.02 to 148 | 0.83 | 0.65 | 0.01 to 51 | 0.85 |
| BMI | Odds ratioc | 1.23 | 0.64 to 2.37 | 0.53 | 1.17 | 0.66 to 2.08 | 0.59 | 0.80 | 0.43 to 1.49 | 0.48 |
| Physical activity | Odds ratioc | 1.53 | 0.88 to 2.66 | 0.13 | 1.47 | 0.69 to 3.13 | 0.31 | 1.59 | 0.70 to 3.64 | 0.27 |
| Healthy diet score | Odds ratiod | 0.93 | 0.52 to 1.68 | 0.81 | 1.22 | 0.56 to 2.63 | 0.61 | 0.66 | 0.27 to 1.60 | 0.35 |
| Total cholesterol | Odds ratioc | 1.49 | 0.95 to 2.33 | 0.09 | 1.44 | 0.79 to 2.64 | 0.23 | 1.55 | 0.82 to 2.96 | 0.18 |
| Blood pressure | Odds ratiod | 1.83 | 1.13 to 2.97 | 0.014 | 1.55 | 0.80 to 3.01 | 0.20 | 2.28 | 1.12 to 4.63 | 0.024 |
| Fasting glucose | Odds ratioc | 0.90 | 0.56 to 1.44 | 0.66 | 0.99 | 0.55 to 1.77 | 0.96 | 0.51 | 0.27 to 0.97 | 0.042 |
| Total LS7 score | Differencea | 0.49 | 0.07 to 0.90 | 0.02 | 0.72 | 0.07 to 1.38 | 0.03 | −0.04 | −0.74 to 0.66 | 0.91 |
| PhQ9 (Mood) | Differencea | 0.34 | −0.33 to 1.02 | 0.32 | 0.38 | −0.68 to 1.43 | 0.48 | 0.85 | −0.27 to 1.97 | 0.14 |
| MMAS (Medication adherence) | Differencea | −0.16 | −0.45 to 0.13 | 0.27 | No subgroup analyses planned | |||||
| Quality of life EQ5D VAS | Differencea | 0.25 | −2.57 to 3.08 | 0.86 | ||||||
| SWL satisfaction with life | Differencea | −0.23 | −1.21 to 0.76 | 0.65 | ||||||
Abbreviations: CVD = cardiovascular disease; HWC = Health and Wellness Coaching.
Adjusted absolute difference at 9 mo between HWC and UC arms in mean change from baseline from linear regression.
Change in RR based on the mean CVD risk at baseline.
Odds ratio at 9 mo from proportional odds regression. The subscores were treated as 3-level ordinal variables. The OR is the factor by which the odds of an outcome increasing by one level (moderate vs poor, good vs moderate) are multiplied in the intervention arm compared with the control arm. The ORs are the same regardless of the starting level (poor or moderate). An OR >1 indicates benefit.
Odds ratio at 9 mo from logistic regression. The subscores were dichotomized. The OR represents the factor by which the odds of a positive outcome are multiplied in the intervention arm as compared with the control arm. An OR >1 indicates benefit.
Secondary Outcomes
Adjusted regression analyses showed a statistically significant positive difference in change from baseline in the total LS7 score between the HWC group and the UC group (0.49, 95% CI [0.07,0.90], p = 0.02) (Table 4), but this difference was only statistically significant in the moderate CVD risk group (0.72, 95% CI [0.07 to 1.38], p = 0.03). There was also statistically significantly greater improvement in blood pressure control (OR = 1.83, 95% CI [1.13 to 2.97], p = 0.01), especially in the high CVD risk group (OR = 2.28, 95% CI [1.12 to 4.63], p = 0.02), where there was also better control of blood glucose (OR = 2.28, 95% CI [1.12 to 4.63], p = 0.02). No statistically significant group differences were found in mood scores, medication adherence (including dosage and types of the medications for the treatment of arterial hypertension, diabetes, and elevated blood cholesterol), quality of life, and satisfaction with life scores (Table 3). No stroke, TIA, or acute CVD event was observed in either group during the follow-up assessments.
Systolic and diastolic blood pressure increased from baseline to 9 months follow-up in both HWC and UC groups (Table 5), but the increases in the HWC group (5 mm Hg and 4 mm Hg, respectively) were statistically significantly smaller than those in the UC group (11 mm Hg and 7 mm Hg, respectively; p = 0.019 and 0.015, respectively), after adjustment for baseline values. At the 9-month post-randomization follow-up, there were no statistically significant changes in mean BMI or concentrations of fasting blood glucose and total cholesterol between the groups. No unintended effects of the HWC intervention were registered; however, the “lost to follow up” rate was twice as high in the HWC group compared with the UC group.
Table 5.
Changes in LS7 Metabolic Risk Measures
| Metric | Usual care | Health and wellness coaching | Adjusted p valuea | ||
| Baseline | 9 mo | Baseline | 9 mo | ||
| Systolic blood pressure, mean mm Hg (SD) | 133 (13.2) | 144 (19.1) | 132 (14.8) | 137 (19.5) | 0.019 |
| Diastolic blood pressure, mean mm Hg (SD) | 80 (8.6) | 87 (11.7) | 78 (9.6) | 82 (11.3) | 0.015 |
| Blood glucose, mean (SD) | 6.9 (3.1) | 6.6 (2.7) | 7.0 (3.1) | 7.0 (2.7) | 0.212 |
| Total blood cholesterol, mean (SD) | 5.2 (1.3) | 5.1 (1.2) | 5.0 (1.2) | 4.7 (1.3) | 0.071 |
| BMI, mean (SD) | 31.7 (7.7) | 31.7 (8.0) | 32.3 (10.2) | 32.8 (10.5) | 0.160 |
p-value to test that the differences in changes from baseline is different from 0, adjusted for baseline.
Lost to Follow-up and Missed Assessments at 9 Months
Fifty-seven baseline characteristics were assessed using t-tests and chi-square tests between participants lost to follow-up or with all data missing at 9 months (“Missing”; n = 52) and those who completed the 9-month assessment (“Assessed”; n = 268). Five of these characteristics displayed a raw p-value smaller than 5% (Table 6).
Table 6.
Baseline Characteristics That Differ at the 5% Level Between Participants Missing and Assessed at 9 Months
| Outcome at baseline | Missing (n = 52) | Assessed (n = 268) | Direction of benefit | Unadjusted p value |
| Mean (SE) | Mean (SE) | |||
| LS7 smoking score (out of 3) | 1.35 (0.93) | 1.66 (0.74) | ↑ | 0.007 |
| LS7 cholesterol score (out of 3) | 1.33 (0.65) | 1.09 (0.63) | ↑ | 0.013 |
| LS7 glucose score (out of 3) | 1.31 (0.81) | 1.04 (0.78) | ↑ | 0.025 |
| CVD risk | 0.14 (0.05) | 0.16 (0.06) | ↓ | 0.045 |
| Count (%) | Count (%) | |||
| No medical condition | 15 (29%) | 42 (16%) | ↑ | 0.038 |
Abbreviations: CVD = cardiovascular disease; SE = standard error.
After adjusting for multiple testing using the Hochberg procedure36 (which is less conservative than the Holm,37 Benjamini and Hochberg,38 or Bonferroni procedures39), none of these differences proved significant at the 5% level. The full set of results is available in the Supplementary materials.
Discussion
This study has not borne out the primary hypothesis because there was no improvement in CVD risk in the moderate-risk group. In patients with a high baseline CVD risk, health and wellness coaching resulted in a 10.9% RRR in the 5-year CVD risk (p = 0.03) compared with UC at 9 months post-randomization. This reduction is inferior to the one posited in the primary hypothesis. However, this treatment effect is clinically significant (number needed to treat was 43). The positive effects of the intervention in the high CVD risk group were consistent with the positive effects of HWC on health and improved well-being of patients with some chronic diseases.10,12,13 However, in the moderate-risk group, there was no improvement in CVD risk possibly because these people were 3–5 years younger than the high-risk group, leading to differences in risk factor and demographic profiles. Because there was no significant difference in the adherence, types, and dosages of medications used for blood pressure and lipid-lowering therapy between the HWC and UC groups, the treatment effect achieved is likely to be related to the behavioral components of the HWC intervention. Age has the greatest effect on CVD risk,40 and it is likely that in high CVD risk people, smaller differences in the exposure to risk factors (for example, the observed better blood pressure control) result in greater reductions in CVD risk than the same level of reductions in people with moderate CVD risk. It is also possible that the PREDICT algorithm did not pick up changes in several of the areas being targeted by the intervention such as lifestyle factors other than smoking, because this content is not well represented in the outcome measure. A recent meta-analysis of multifactorial lifestyle interventions for primary prevention of CVD also reported better CVD risk improvement in high CVD risk groups.41
Elevated blood pressure is the single most important modifiable risk factor of stroke, and our findings of improved of blood pressure control in the HWC group compared with the UC group (overall and in moderate and high CVD risk groups separately) highlights the potential of the HWC intervention for primary stroke prevention. Although blood pressure increased in both groups over the duration of the study, this increase was smaller in the HWC group. Because the percentage of study participants receiving blood pressure-lowering medications was not statistically significantly different between the groups, the reason for increasing blood pressure in both groups remains unclear. The p-values were not corrected for multiple comparisons, and the difference may be due to chance. Our trial was not powered to determine the effect of the intervention on other individual lifestyle risks. The findings of an approximately 0.5-point increase in the overall LS7 score and a 0.7-point increase in the moderate-risk group are equivalent to a 4% and 5% annual stroke incidence reduction, respectively.17
We did not find any significant differences in mood, satisfaction with life, quality of life, or medication adherence scores. This may be due to the floor effect of screening, as those with depression and anxiety at screening were not eligible to participate. Hence, the mean mood scores in both groups at baseline were in the normal-to-mild range, which means further improvement was unlikely. The medication adherence score at baseline was low in both groups and did not improve at 9 months. The reasons for this are unclear, and ways to target medication adherence in HWC need to be explored further. One possibility is that participants' behavior may have been modified by knowing they were in a trial and would be asked about their medications.
The study had several strengths and limitations. The main strength was that the sample was ethnically diverse and covered both men and women of age 35+ years, thus enhancing generalizability of the findings for people with moderate-to-high CVD risk. HW coaches were community workers demonstrating the ability of non-health professionals to be trained to deliver HWC. The HWC intervention was able to be delivered predominantly by telephone, making it more feasible to roll out into the community. Finally, the target sample population was at an increased risk of CVD, hence appropriate for a multifactorial lifestyle modification intervention. The main weakness of the study was that while CVD risk was the primary outcome, the Framingham-based calculation of CVD risk assigns greater weight to age and ethnicity, which are not amenable to lifestyle changes. In hindsight, addressing primary stroke prevention in this population required a metric focused on lifestyle. The LS7 and more detailed measures of lifestyle risk may be more appropriate measures in future studies, with a longer term of follow-up (ideally, with the hard primary end points, such as new strokes/TIA and acute CVD events). The intervention was not tailored to be culturally responsive to Māori, Pacific, and Asian groups, and future HWC interventions should be co-designed with relevant cultural advisors, so they are better tailored to different cultural groups.
In conclusion, HWC has the potential to improve cardiovascular health in those with high cardiovascular disease risk. Further work is required to improve the efficacy of HWC in those at moderate cardiovascular disease risk.
Acknowledgment
The PREDICT software license was provided by Enigma Solutions (enigma.co.nz/). The authors thank DR Takayoshi Ikeda who helped us with early statistical analysis of the data. They also acknowledge the participating medical practices for their support.
Appendix. Authors
| Name | Location | Contribution |
| Rita V. Krishnamurthi, BSc, MApplSc, PhD | National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand | Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data |
| Alain C. Vandal, PhD | Department of Statistics, The University of Auckland; Ko Awatea, Counties Manukau Health, New Zealand | Analysis or interpretation of data |
| Suzanne Barker-Collo, MA, PhD | Department of Psychology, The University of Auckland, New Zealand | Study concept or design; analysis or interpretation of data |
| Susan Mahon, PhD | National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand | Major role in the acquisition of data; analysis or interpretation of data |
| P. Alan Barber, MD, PhD | Department of Neurology, The University of Auckland, New Zealand | Study concept or design; analysis or interpretation of data |
| Bruce Arroll, MD, PhD | Department of General Practice, The University of Auckland, New Zealand | Study concept or design; analysis or interpretation of data |
| Elaine Rush, PhD | National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand | Study concept or design; analysis or interpretation of data |
| Hinemoa Elder, PhD | Te Whare Wānanga o Awanuiārangi, Auckland, New Zealand | Study concept or design; analysis or interpretation of data |
| Valery L. Feigin, MD, PhD, FAAN | National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand | Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data |
Study Funding
National Science Challenge New Zealand “Ageing Well” (Grant Reference UOOX1508). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit the manuscript.
Disclosure
The authors report no relevant disclosures. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp.
References
- 1.Larsson SC, Akesson A, Wolk A. Primary prevention of stroke by a healthy lifestyle in a high-risk group. Neurology. 2015;84(22):2224-2228. doi: 10.1212/WNL.0000000000001637 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gorelick PB. Primary prevention of stroke: impact of healthy lifestyle. Circulation. 2008;118(9):904-906. doi: 10.1161/CIRCULATIONAHA.108.800169 [DOI] [PubMed] [Google Scholar]
- 3.Chiuve SE, Rexrode KM, Spiegelman D, Logroscino G, Manson JE, Rimm EB. Primary prevention of stroke by healthy lifestyle. Circulation. 2008;118(9):947-954. doi: 10.1161/CIRCULATIONAHA.108.781062 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lin MP, Ovbiagele B, Markovic D, Towfighi A. "Life's Simple 7" and long-term mortality after stroke. J Am Heart Assoc. 2015;4(11):e001470. doi: 10.1161/JAHA.114.001470 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.LeFevre ML, U.S. Preventive Services Task Force. Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;161(8):587-593. doi: 10.7326/M14-1796 [DOI] [PubMed] [Google Scholar]
- 6.Linden A, Butterworth SW, Prochaska JO. Motivational interviewing‐based health coaching as a chronic care intervention. J Eval Clin Pract. 2010;16(1):166-174. doi:doi: 10.1111/j.1365-2753.2009.01300.x [DOI] [PubMed] [Google Scholar]
- 7.Seligman ME, Steen TA, Park N, Peterson C. Positive psychology progress: empirical validation of interventions. Am Psychologist. 2005;60(5):410-421. doi: 10.1037/0003-066X.60.5.410 [DOI] [PubMed] [Google Scholar]
- 8.Huffman MH. Health coaching: a fresh approach for improving health outcomes and reducing costs. AAOHN J. 2010;58(6):245-250; quiz 251-252. doi: 10.3928/08910162-20100526-02 [DOI] [PubMed] [Google Scholar]
- 9.Jefferson V, Jaser SS, Lindemann E, et al. Coping skills training in a telephone health coaching program for youth at risk for type 2 diabetes. J Pediatr Health Care. 2011;25(3):153-161. doi: 10.1016/j.pedhc.2009.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kivelä K, Elo S, Kyngäs H, Kääriäinen M. The effects of health coaching on adult patients with chronic diseases: a systematic review. Patient Educ Couns. 2014;97(2):147-157. doi: 10.1016/j.pec.2014.07.026 [DOI] [PubMed] [Google Scholar]
- 11.Kreitzer MJ, Sierpina VS, Lawson K. Health coaching: innovative education and clinical programs emerging. Explore (NY). 2008;4(2):154-155. doi: 10.1016/j.explore.2007.12.007 [DOI] [PubMed] [Google Scholar]
- 12.Wolever RQ, Eisenberg DM. What is health coaching anyway? Standards needed to enable rigorous research. Arch Intern Med. 2011;171(22):2017-2018. doi: 10.1001/archinternmed.2011.508 [DOI] [PubMed] [Google Scholar]
- 13.Vale MJ, Jelinek MV, Best JD, et al. ; COACH Study Group. Coaching patients on achieving cardiovascular health (COACH): a multicenter randomized trial in patients with coronary heart disease. Arch Intern Med. 2003;163(22):2775-2783. doi: 10.1001/archinte.163.22.2775 [DOI] [PubMed] [Google Scholar]
- 14.Hansson L, Hedner T, Dahlöf B. Prospective randomized open blinded end-point (PROBE) study. A novel design for intervention trials. Prospective Randomized Open Blinded End-Point. Blood Press. 1992;1(2):113-119. doi: 10.3109/08037059209077502 [DOI] [PubMed] [Google Scholar]
- 15.New Zealand Guidelines Group. Evidence-based Best Practice Guideline: The Assessment and Management of Cardiovascular Risk. New Zealand Guidelines Group; 2003. [Google Scholar]
- 16.Mahon S, Krishnamurthi R, Vandal A, et al. Primary prevention of stroke and cardiovascular disease in the community (PREVENTS): methodology of a health wellness coaching intervention to reduce stroke and cardiovascular disease risk, a randomized clinical trial. Int J Stroke. 2018;13(2):223-232. doi: 10.1177/1747493017730759 [DOI] [PubMed] [Google Scholar]
- 17.Kulshreshtha A, Vaccarino V, Judd SE, et al. Life's simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke. 2013;44:1909-1914. doi: 10.1161/STROKEAHA.111.000352 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Pylypchuk R, Wells S, Kerr A, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet. 2018;391(10133):1897-1907. doi: 10.1016/S0140-6736(18)30664-0 [DOI] [PubMed] [Google Scholar]
- 19.Wells S, Riddell T, Kerr A, et al. Cohort profile: the PREDICT cardiovascular disease cohort in New Zealand primary care (PREDICT-CVD 19). Int J Epidemiol. 2017;46(1):22. doi: 10.1093/ije/dyv312 [DOI] [PubMed] [Google Scholar]
- 20.Kroenke K, Spitzer R, Williams J. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613. doi: 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Witt E. Overview of ART Database. AUT University; 2017. [Google Scholar]
- 22.Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi: 10.1136/bmj.g1687 [DOI] [PubMed] [Google Scholar]
- 23.Heart Foundation of New Zealand. Lowering Your Risk of Heart Attack and Stroke—Booklet. Accessed August 26, 2019. heartfoundation.org.nz/resources/lowering-your-risk-of-heart-attack-and-stroke-booklet. [Google Scholar]
- 24.Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74. doi: 10.1097/00005650-198601000-00007 [DOI] [PubMed] [Google Scholar]
- 25.Szende A, Janssen M, Cabases J, Ramos Goñi J. Self-reported population health: an international perspective based on EQ-5D. Value in Health. 2013;16(7):A464. doi: 10.1016/j.jval.2013.08.812 [DOI] [PubMed] [Google Scholar]
- 26.Diener E, Chan M. Happy people live longer: subjective well-being contributes to health and longevity. Appl Psychol Health Well-Being. 2011;3:1-43. doi:doi: 10.1111/j.1758-0854.2010.01045.x [DOI] [Google Scholar]
- 27.Carpenter JR, Kenward MG. Missing data in randomised controlled trials: a practical guide. In: Health Technology Assessment Methodology Programme; 2007. [Google Scholar]
- 28.Wittes J. Sample size calculations for randomized controlled trials. Epidemiol Rev. 2002;24(1):39-53. doi: 10.1093/epirev/24.1.39 [DOI] [PubMed] [Google Scholar]
- 29.Cox JL, Carr B, Vallis TM, Szpilfogel C, O'Neill BJ. A novel approach to cardiovascular health by optimizing risk management (ANCHOR): a primary prevention initiative examining the impact of health risk factor assessment and management on cardiac wellness. Can Journal Cardiol. 2011;27(6):809-817. doi: 10.1016/j.cjca.2011.04.013 [DOI] [PubMed] [Google Scholar]
- 30.Mc Namara KP, George J, O'Reilly SL, et al. Engaging community pharmacists in the primary prevention of cardiovascular disease: protocol for the pharmacist assessment of adherence, risk and treatment in cardiovascular disease (PAART CVD) pilot study. BMC Health Serv Res. 2010;10:264. doi: 10.1186/1472-6963-10-264 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Jackson R, Lawes CMM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk. Lancet. 2005;365(9457):434-441. doi: 10.1016/S0140-6736(05)17833-7 [DOI] [PubMed] [Google Scholar]
- 32.Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%. BMJ. 2003;326:1419. doi: 10.1136/bmj.326.7404.1419 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Czernichow S, Zanchetti A, Turnbull F, et al. ; Blood Pressure Lowering Treatment Trialists' Collaboration. The effects of blood pressure reduction and of different blood pressure-lowering regimens on major cardiovascular events according to baseline blood pressure: meta-analysis of randomized trials. J Hypertens. 2011;29(1):4-16. doi: 10.1097/HJH.0b013e32834000be [DOI] [PubMed] [Google Scholar]
- 34.Turnbull F, Blood Pressure Lowering Treatment Trialists' Collaboration. Effects of different blood-pressure-lowering regimens on major cardiovascular events: results of prospectively-designed overviews of randomised trials. Lancet. 2003;362(9395):1527-1535. doi: 10.1016/s0140-6736(03)14739-3 [DOI] [PubMed] [Google Scholar]
- 35.Blom JW, Muth C, Glasziou P, et al. Describing deprescribing trials better: an elaboration of the CONSORT statement. J Clin Epidemiol. 2020;127:87-95. doi: 10.1016/j.jclinepi.2020.07.011 [DOI] [PubMed] [Google Scholar]
- 36.Hochberg Y. A sharper bonferroni procedure for multiple tests of significance. Biometrika. 1988;75(4):800-802. doi: 10.2307/2336325 [DOI] [Google Scholar]
- 37.Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65-70. [Google Scholar]
- 38.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57(1):289-300. doi: 10.1111/j.2517-6161.1995.tb02031.x [DOI] [Google Scholar]
- 39.Dunn OJ. Multiple comparisons among means. J Am Stat Assoc. 1961;56(293):52-64. doi: 10.2307/2282330 [DOI] [Google Scholar]
- 40.Dhingra R, Vasan RS. Age as a risk factor. Med Clin North Am. 2012;96(1):87-91. doi: 10.1016/j.mcna.2011.11.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Sisti L, Dajko M, Campanella P, Shkurti E, Ricciardi W, De Waure C. The effect of multifactorial lifestyle interventions on cardiovascular risk factors: a systematic review and meta-analysis of trials conducted in the general population and high risk groups. Prev Med. 2018;109:82-97. doi: 10.1016/j.ypmed.2017.12.027 [DOI] [PubMed] [Google Scholar]