This article has Open Peer Review reports available.
Health coaching by telephony to support self-care in chronic diseases: clinical outcomes from The TERVA randomized controlled trial
© Patja et al.; licensee BioMed Central Ltd. 2012
Received: 6 November 2011
Accepted: 14 April 2012
Published: 10 June 2012
The aim was to evaluate the effect of a 12-month individualized health coaching intervention by telephony on clinical outcomes.
An open-label cluster-randomized parallel groups trial. Pre- and post-intervention anthropometric and blood pressure measurements by trained nurses, laboratory measures from electronic medical records (EMR). A total of 2594 patients filling inclusion criteria (age 45 years or older, with type 2 diabetes, coronary artery disease or congestive heart failure, and unmet treatment goals) were identified from EMRs, and 1535 patients (59%) gave consent and were randomized into intervention or control arm. Final analysis included 1221 (80%) participants with data on primary end-points both at entry and at end. Primary outcomes were systolic and diastolic blood pressure, serum total and LDL cholesterol concentration, waist circumference for all patients, glycated hemoglobin (HbA1c) for diabetics and NYHA class in patients with congestive heart failure. The target effect was defined as a 10-percentage point increase in the proportion of patients reaching the treatment goal in the intervention arm.
The proportion of patients with diastolic blood pressure initially above the target level decreasing to 85 mmHg or lower was 48% in the intervention arm and 37% in the control arm (difference 10.8%, 95% confidence interval 1.5–19.7%). No significant differences emerged between the arms in the other primary end-points. However, the target levels of systolic blood pressure and waist circumference were reached non-significantly more frequently in the intervention arm.
Individualized health coaching by telephony, as implemented in the trial was unable to achieve majority of the disease management clinical measures. To provide substantial benefits, interventions may need to be more intensive, target specific sub-groups, and/or to be fully integrated into local health care.
ClinicalTrials.gov Identifier: NCT00552903
Diabetes and cardiovascular diseases represent large and costly chronic healthcare challenges . Preventative measures can effectively reduce costs . Despite differences between different conditions, the expectations on the patients are similar: to cope with multiple medications and co-morbidities, to alter behavior, to deal with social and psychological impacts of symptoms and to interact with medical care [3, 4].
Health care providers have a difficult task in trying to manage chronic disease care in complex service systems that are poorly designed to motivate, equip and empower patients to behavior changes [5–7]. Resources should aim at maximized health gains, and this requires reorientation of services . High expectations are put on information technology solutions that have been shown highly effective in promoting lifestyle changes . So far, comprehensive efforts to assess the impact of incorporating a range of IT tools in chronic disease management have been targeting single disease groups, such as CHD [10, 11], heart failure  or diabetes [13, 14] Taylor et al. 2003, but studies with several disease groups and/or co-morbidities are lacking.
While technology can be an effective way to improve reach of disease management interventions, still the content is more important. Health coaching, a collaborative process characterized by motivational communication, patient-defined goals related to disease management, and patient acceptance of accountability for decisions made  can utilize different sets of self-management tools (SMTs) to promote adoption of an active role in self-care by the patient . Health coaching can improve quality, effectiveness and cost-effectiveness of disease management . The TERVA trial is the first large randomized controlled trial to simultaneously evaluate tele-coaching in a real-world health care setting in three patient groups: congestive heart failure (CHF), coronary artery disease (CAD) and type 2 diabetes mellitus (T2D). The aim of the trial was to assess the effect of health coaching on clinical outcomes (risk determinants) after one-year intervention.
Primary and secondary end points of TERVA trial
Primary end points
▪ Provider-measured BP ≤140/85 mmHg
▪ Total cholesterol ≤4.5 mmol/L
▪ LDL ≤2.5 mmol/L
▪ Waist circumference ≤94 cm for men and ≤80 cm for women – later revised as 90 cm for women and 100 cm for men based on national guidelines
For congestive heart failure an additional end-point:
▪ Improved or maintained NYHA class
For participants with T2D:
▪ HbA1c ≤7%
Research nurses, unaware of the allocation, measured blood pressure and waist circumference in both arms. The laboratory results were extracted from the electronic medical records (EMR) at both entry and end of the intervention (at entry between 3 months before to 1 month after and at end 11 to 15 months from date of consent). NYHA-class was obtained from study questionnaires at entry and end of follow-up.
Identification and enrollment
Eligibility and exclusion criteria of TERVA trial
Eligibility criteria for enrollment included:
1. Residents in the region of Päijät-Häme aged 45 years or older
2. One of the following diagnose
a. Heart failure with NYHA II or III, and a history of hospital admission for heart failure within the last 2 years
b. History of myocardial infarction or cardiac revascularization procedure, and one of the following (treated or untreated): blood pressure above 140/85 mmHg, total serum cholesterol concentration
>4.5 mmol/L, serum LDL concentration >2.5 mmol/L
c. T2D on medication and serum HbA1c >7% without clinically evident cardiovascular diseases e.g. MI, stroke, peripheral vascular disease
● Inability to cooperate or participate
● Life expectancy less than 1 year
● Patients with major elective surgery planned within 6 months
● Patient has had major surgery within the last 2 months
A sample size of 1250 patients was calculated to provide adequate statistical power (1-β ≥ 0.8) for detecting a 10 percentage point difference between the intervention arms (with 6 coaches) with conservative assumptions (α = 0.05 two-sided, 50% of the patients in the control arm would reach target, a 10% drop out rate and 10% of the subjects not evaluable at the end of the trial), as long as the intracluster correlation did not exceed 0.01 .
Data analyses were conducted using multilevel methods (generalized linear mixed models) to account for the clustered design. The trial data had a two-level structure, where the health coaches constituted an upper level, within which the individual patients were distributed allowing for correlation at individual level within a cluster (variance components at the two levels).
Baseline data available from patients who were allocated to the study (intervention = 1034, control = 501)
Type 2 Diabetes
Coronary artery disease
Congestive heart failure
Number of patients
Sex (% male)
Age at self reported year of diagnosis
Self-reported duration of disease
Body mass index
Waist circumference (M/F)
109.9 (14.4)/ 106.8 (15.5)
110.1 (12.8)/ 104.8 (15.1)
100.3 (11.0)/ 95.1 (12.8)
102.9 (11.7)/ 91.5 (12.6)
106.7 (12.8)/ 94.1 (14.6)
108.0 (17.9)/ 89.6 (17.3)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Serum total cholesterol (mmol/l)
Serum HDL cholesterol (mmol/l)
Serum LDL cholesterol (mmol/l)
Lipid lowering medication (%)
Daily smokers (%)
Oral antidiabetic drug and insulin (%)
Oral antidiabetic drug (%)
Ethical approval and trial number
Written informed consent was obtained from all participants prior to enrollment into the project. The study protocol was approved by the Ethics Committee of the PHSSHD and registered (ClinicalTrials.gov Identifier: NCT00552903).
In the intervention arm, 48.1% of the patients (156/324) initially above the target level of diastolic blood pressure of 85 mmHg reached this value, while for the control arm the proportion was 37.3% (62/166). The 10.8% (95% confidence interval (CI) 1.5–19.7%) difference in proportion of patients who reached the goal was statistically significant and gave a number needed to treat of 10 (CI 5–66). Of the patients with a systolic blood pressure above the target level of 140 mmHg at baseline, 35.9% (143/398) in the intervention arm and 31.0% (58/187) in the control arm reached the target (p = 0.24).
Proportion (%) of those patients reaching targets in primary end points among those exceeding these values at baseline in the analysed population (intervention = 816, controls = 405)
Type 2 Diabetes
Coronary artery disease
Congestive heart failure
Waist circumference (<90cm women, <100cm men)
Systolic blood pressure (<140mmHg)
Diastolic blood pressure (<85mmHg)
Serum total cholesterol (<4.5mmol/l)
Serum LDL cholesterol (<2.5mmol/l)
NYHA class (similar or improved)
Target reached in at least one primary endpoint*
The goal for total cholesterol reduction was reached more often in control arm than in intervention arm (p = 0.64) as was the LDL cholesterol target (≤2.5 mmol/l) (p = 0.68). For patients with CHF, NYHA class remained similar or improved in both arms (p = 0.39). The proportion of patients achieving at least one of the defined primary objectives was 50.0% (371/742) in the intervention and 46.1% in the control arm (170/369, p = 0.22). Within the intervention arm, no substantial differences were found between subjects assigned to different nurses (intracluster correlation 0.01).
The TERVA trial was carried out in a real life setting and aimed at increasing the proportion of intervention patients reaching at least one of the predefined targets (blood pressure, HbA1c, waist circumference, NYHA class or total cholesterol) by 10% compared to controls. There was a small, non-significant improvement in the proportion of patients who reached at least one of the primary endpoints for both the whole study population, and for each of the disease area subgroups separately. However, the difference reached the predefined 10% difference between the groups only for the CHF patients. An encouraging finding is the high adherence, nearly 90% of the patients remained in the trial during the intervention (similar to the control arm). Further analysis of the intervention arm will define how well patients could achieve the goals that they actually set at the beginning of the intervention.
Chronic disease management is a complex process urging multiple simultaneous changes in self-care, in health behavior, and in the interaction with medical care [3, 21]. A complex intervention such as ours that targets these multiple behaviors cannot be compared to single-behavior interventions such as smoking cessation, medication adherence, or physical activity interventions. Despite these methodological complexities, little differences were found between subjects assigned to different nurses, indicating consistency in delivering the intervention. Further, health behavior changes may have a delayed impact or may impact the risk of cardiovascular diseases independently of clinical outcomes . These reasons may partly explain that we did not meet our study objectives. Another possibility is that the intensity of the intervention was too low to sufficiently cover multiple behaviors, as recent evidence suggests that telephony interventions targeting only physical activity or/and diet produce most favorable effects when the number of calls is 12 or more . Several previous studies have assessed the effect of telephony interventions on similar outcomes as ours [6, 7, 14, 22]. Also these trials have shown modest improvement in clinical and health behavior outcomes.
This study aimed to evaluate an intervention within the public health care system and occupationally active patients were underrepresented, as they are mostly covered by occupational health services , and retired patients with more severe disease are overrepresented. The T2D patients in the trial (selected based on HbA1c >7% within 6 months prior to inclusion) represented approximately one third of the T2D patients in the region [24, 25]. Of them 28% had HbA1c >7% at the start of the intervention, which is comparable to the population-based studies of T2D patients , suggesting that the participants are representative of the target population. Davidson concluded in his review the key success factor in diabetes care being specially trained nurses or pharmacists and perhaps one reason for modest results was that those in treatment were receiving already specialist nurse care  and added value of telephony was limited.
We included three different disease areas with variable disease severity. The mean HbA1c was only 7.5% in intervention arm and 7.7% in control arm, with 28% and 25% with baseline HbA1c >7 respectively, and disease history of 9.2 and 10.3 years. The large proportion of T2D patients with HbA1c at the target level at enrollment was due to the fact that the patients were originally screened from primary care EMRs, and had frequently improved by the time of enrollment, which could be up to 6 months later. Also, the end of study HbA1c measurement could potentially be up to 10 months after the intervention. The abstraction of the laboratory data from EMRs instead of a strict measurement protocol was motivated by the pragmatic nature of the trial, but in the low proportion of subjects with such data at the end of the study reduced the power (despite reaching the target sample size) and could introduce bias, as assessments were not prescribed randomly. This limitation renders the findings related to laboratory data difficult interpret meaningfully. Further, the targets for primary end-points, for instance waist circumference, which were based on systematic reviews of behavioral risk factor and disease management interventions, may have been too stringent . Finally, the intervention was not coordinated with other health care providers, but rather added on top of the existing services. Some specialist diabetes nurses expressed a concern that health coaching was challenging their professional role, but no assessments were carried out to objectively measure health professionals’ perceptions of the coaching program. Therefore, we can only speculate on the effect of the perceived competition on the results. However, it should be emphasized that the changes that were detected under these circumstances, demonstrate effects achieved in a real life setting.
The results of this trial are inconclusive, as we did meet the primary end-point for diastolic blood pressure only with non-significant improvement in systolic blood pressure and waist circumference and no improvement in glycemic control, cholesterol or NYHA class. The overall lack of efficacy of health coaching may be related to the target population, coaching procedures and the duration of the follow-up time, and will be further explored in longer follow-up and sub-group analyses, as well as analysis of behavioral outcomes. Methodological factors and too strict primary targets may contribute to inability to meet all the predetermined primary objectives. Further, the primary analysis focused on efficacy, and analysis on resource utilization and cost-efficacy need to be performed to fully clarify the role of health coaching by telephone in this setting.
We thank Academy Professor Hannu Oja, School of Health Sciences, University of Tampere for advice on statistical methods. Hannu.email@example.com.
Joint Authority for Päijät-Häme Social and Health Care
Sitra - the Finnish Innovation Fund
TEKES - the Finnish Funding Agency for Technology and Innovation
- Geneau R, Stuckler D, Stachenko S, McKee M, Ebrahim S, Basu S, Chockalingham A, Mwatsama M, Jamal R, Alwan A, Beaglehole R: Raising the priority of preventing chronic diseases: a political process. Lancet. 2010, 13: 1689-1698.View ArticleGoogle Scholar
- Cecchini M, Sassi F, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D: Tackling of unhealthy diets, physical inactivity, and obesity: Health effects and cost-effectiveness. Lancet. 2010, 20: 1775-1784.View ArticleGoogle Scholar
- Daar AS, Singer PA, Persad DL, Pramming SK, Matthews DR, Beaglehole R, Bernstein A, Borysiewicz LK, Colagiuri S, Ganguly N, Glass RI, Finegood DT, Koplan J, Nabel EG, Sarna G, Sarrafzadegan N, Smith R, Yach D, Bell J: Grand challenges in chronic non-communicable diseases. Nature. 2007, 22: 494-496.View ArticleGoogle Scholar
- Davidson MB: How our current medical care system fails people with diabetes: Lack of timely, appropriate clinical decisions. Diabetes Care. 2009, 2: 370-372.View ArticleGoogle Scholar
- Choe HM, Mitrovich S, Dubay D, Hayward RA, Krein SL, Vijan S: Proactive case management of high-risk patients with type 2 diabetes mellitus by a clinical pharmacist: a randomized controlled trial. Am J Manag Care. 2005, 11: 253-260.PubMedGoogle Scholar
- Costa BM, Fitzgerald KJ, Jones KM, Dunning Am T: Effectiveness of IT-based diabetes management interventions: a review of the literature. BMC Fam Pract. 2009, 10: 72-10.1186/1471-2296-10-72.View ArticlePubMedPubMed CentralGoogle Scholar
- Krishna S, Boren SA, Balas EA: Healthcare via cell phones: a systematic review. Telemed J E Health. 2009, 15: 231-240. 10.1089/tmj.2008.0099.View ArticlePubMedGoogle Scholar
- Wagner EH, Groves T: Care for chronic diseases. BMJ. 2002, 26: 913-914.View ArticleGoogle Scholar
- Goode AD, Reeves MM, Eakin EG: Telephone-delivered interventions for physical activity and dietary behavior change: an updated systematic review. Am J Prev Med. 2012, 42: 81-88. 10.1016/j.amepre.2011.08.025.View ArticlePubMedGoogle Scholar
- DeBusk RF, Miller NH, Superko HR, Dennis CA, Thomas RJ, Lew HT, Berger WE, Heller RS, Rompf J, Gee D, Kraemer HC, Bandura A, Ghandour G, Clark M, Shah RV, Fisher L, Taylor CB: A case-management system for coronary risk factor modification after acute myocardial infarction. Ann Intern Med. 1994, 120: 721-729.View ArticlePubMedGoogle Scholar
- Vale MJ, Jelinek MV, Best JD, Dart AM, Grigg LE, Hare DL, Ho BP, Newman RW, McNeil JJ: 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: 2775-2783. 10.1001/archinte.163.22.2775.View ArticlePubMedGoogle Scholar
- McAlister FA, Stewart S, Ferrua S, McMurray JJ: Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. J Am Coll Cardiol. 2004, 44: 810-819.PubMedGoogle Scholar
- Taylor CB, Miller NH, Reilly KR, Greenwald G, Cunning D, Deeter A, Abascal L: Evaluation of a nurse-care management system to improve outcomes in patients with complicated diabetes. Diabetes Care. 2003, 26: 1058-1063. 10.2337/diacare.26.4.1058.View ArticlePubMedGoogle Scholar
- Bu D, Pan E, Walker J, Adler-Milstein J, Kendrick D, Hook JM, Cusack CM, Bates DW, Middleton B: Benefits of information technology-enabled diabetes management. Diabetes Care. 2007, 30: 1137-1142. 10.2337/dc06-2101.View ArticlePubMedGoogle Scholar
- Hayes E, McCahon C, Panahi MR, Hamre T, Pohlman K: Alliance not compliance: Coaching strategies to improve type 2 diabetes outcomes. J Am Acad Nurse Pract. 2008, 20: 155-162. 10.1111/j.1745-7599.2007.00297.x.View ArticlePubMedGoogle Scholar
- Barrett MJ: Patient self-management tools: An overview. Californian Healthc Found. 2005, http://www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/P/PDF%20PatientSelfManagementToolsOverview.pdf [read 6.11.2011]Google Scholar
- Olsen JM, Nesbitt BJ: Health coaching to improve healthy lifestyle behaviors: An integrative review. Am J Health Promot. 2010, 25: e1-e12.View ArticlePubMedGoogle Scholar
- Michie S, Abraham C, Whittington C, McAteer J, Gupta S: Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychology. 2009, 28: 690-701.View ArticlePubMedGoogle Scholar
- Rollnick S, Miller WR, Butler CC: Motivational Interviewing in Health Care: Helping Patients Change Behavior. 2008, The Guilford Press, New YorkGoogle Scholar
- Tokola K, Larocque D, Nevalainen J, Oja H: Power, sample size and sampling cost for clustered data. Stat Probab Lett. 2011, 81: 852-860. 10.1016/j.spl.2011.02.006.View ArticleGoogle Scholar
- Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D, et al: Framework for design and evaluation of complex interventions to improve health. BMJ. 2000, 321: 694-696. 10.1136/bmj.321.7262.694.View ArticlePubMedPubMed CentralGoogle Scholar
- Kim SI, Kim HS: Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. Int J Med Inf. 2008, 77: 399-404. 10.1016/j.ijmedinf.2007.07.006.View ArticleGoogle Scholar
- Häkkinen U, Alha P: Health Service Utilization and its Socioeconomic Determinants. Health 2000 Survey. 2006, National Public Health Institute, HelsinkiGoogle Scholar
- Absetz P, Saaristo T, Viitasalo K, Peltonen M: Implementation of prevention of type 2 diabetes – experiences from Finland. Diabetes Prevention in Practice. Edited by: Schwarz P, Reddy P, Greaves C, Dunbar J, Schwarz J. 2010, Tumaini Institute for Prevention Management, DresdenGoogle Scholar
- Absetz P, Valve R, Oldenburg B, Heinonen H, Nissinen A, Fogelholm M, Ilvesmäki V, Talja M, Uutela A: Type 2 diabetes prevention in the “real world”: one-year results of the GOAL implementation trial. Diabetes Care. 2007, 30: 2465-70. 10.2337/dc07-0171.View ArticlePubMedGoogle Scholar
- Oldenburg B, Absetz P, Chan CKY: Behavioral interventions for prevention and management of chronic disease. Handbook of Behavioral Medicine: Methods and Applications. Edited by: Steptoe A, Freedland KE, Jennings JR, Llabre MM, Manuck SB, Susman EJ. 2010, Springer, Philadelphia, 969-988.View ArticleGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/12/147/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.