Min Tao, PhD

 We aimed to describe and contrast the targeting methods and engagement outcomes for health plan—delivered disease management with those of a provider-delivered care management program.

 Health plan epidemiologists partnered with university health services researchers to conduct a quasi-experimental, mixed-methods study of a 2-year pilot. We used semi-structured interviews to assess the characteristics of program-targeting strategies, and calculated target and engagement rates from clinical encounter data.

Five physician organizations (POs) with 51 participating practices implemented care management. Health plan member lists were sent monthly to the practices to accept patients, and then the practices sent back data reports regarding targeting and engagement in care management. Among patients accepted by the POs, we compared those who were targeted and engaged by POs with those who met health plan targeting criteria.

The health plan’s targeting process combined claims algorithms and employer group preferences to identify candidates for disease management; on the other hand, several different factors influenced PO practices’ targeting approaches, including clinical and personal knowledge of the patients, health assessment information, and availability of disease-relevant programs. Practices targeted a higher percentage of patients for care management than the health plan (38% vs 16%), where only 7% of these patients met the targeting criteria of both. Practices engaged a higher percentage of their targeted patients than the health plan (50% vs 13%).

 The health plan’s claims-driven targeting approach and the clinically based strategies of practices both provide advantages; an optimal model may be to combine the strengths of each approach to maximize benefits in care management.

Physician organizations were highly variable and differed from the health plan in their approach to targeting patients and delivering care management.

Care management programs administered in conjunction with patients’ primary care clinical settings yielded a higher percentage and different subgroup of patients who were targeted compared with a health plan’s disease management program, with only a 7% overlap.

Both provider-delivered and health plan-delivered care management offer advantages: the former benefits from a personal relationship with patients and important clinical information, while the latter has patients’ overall healthcare utilization history, including with providers unknown to primary care.

Americans are increasingly plagued by chronic disease,

 and evidence suggests that not all patients are receiving self-care support for managing their disease.

 Care management is a patient-centered approach to “assist patients and their support systems in managing medical conditions more effectively,”

 and includes patient education, goal setting, and self-management support. Active involvement of patients in their care is fundamental to the Patient-Centered Medical Home

 (PCMH) Model and the Chronic Care Model.

 However, not all individuals with chronic disease require services beyond the usual care received from their providers. Therefore, methods for successfully targeting patients to engage them in care management are increasingly important to providers, health plans, and employer groups who must optimally allocate resources.

	Targeting involves identifying patients who may benefit from care management services and offering these services to them. The structure, targeting strategies, and care delivery mechanisms of care management programs are based on the goals of the organizations administering the programs. Health plans offer disease management programs to reduce costs associated with chronic disease—related adverse events among their members; their targeting strategies are driven by assessments of predicted cost risk from claims data and by employer customer preferences. Outreach and care management are delivered by trained registered nurses, primarily by phone. Although health plan disease management programs have been effective for some populations,

 they are criticized for not being integrated with the patient’s primary care physician (PCP).

	Alternatively, many primary care practices and physician groups are developing care management programs that are coordinated with patients’ ongoing care. These programs combine in-person, electronic, and phone-based management, and utilize care managers who are either embedded within the practice or located off-site. Patients are offered care management by a member of the practice team, such as a PCP, care manager, or medical assistant.

Provider-Delivered Care Management Pilot Program

In 2010, a large Midwestern health plan partnered with 5 physician organizations (POs) to implement a 2-year Provider Delivered Care Management (PDCM) pi-lot program. The pilot aimed to improve the health of chronically ill health plan members by financially supporting POs and their affiliated primary care practices in delivering care management, essentially reducing the number of members managed by the health plan’s internal disease management program. Two POs used pilot funding to develop new care management programs, while 3 expanded their existing programs. POs assumed full responsibility for administering their programs. This work developed from the plan’s overall approach of partnering with primary care to improve the quality of care. This includes a PCMH program that offers incentives to POs for increasing medical home-related capabilities, including care management, within primary care practices.

	While many recognize the importance of targeting strategies for successful care management

including the potential benefit of predictive modeling

—there is sparse literature examining targeting options. In this quasiexperimental, mixed-methods study, we aimed to describe and contrast the targeting methods of the health plan model with the PDCM models to address the gap in the literature. The study was approved by the Michigan State University and the University of Michigan Institutional Review Boards.

The 5 POs selected 52 of their primary care practices for the PDCM pilot those with the capability and resources to deliver care management—with the majority (n = 42) having earned PCMH designation from the health plan.

 Given their advanced level on the PCMH spectrum, they were not representative of the nonselected practices within the pilot POs; however, they are likely similar to other PCMH practices with care management capabilities. One practice dropped out of the study in the first year and was excluded from analyses. Adult (18 years and older) health plan members were eligible for the study when they were identified as having a care relationship with a participating PCP in one of the selected practices at some time during the 2-year pilot, and had health plan-delivered disease management coverage under their employer group benefit. A care relationship was inferred via a claims-based algorithm that assessed 24 months of professional claims data for specific evaluation and management services to determine the PCP most responsible for a member’s care.

	A monthly list of eligible members was sent to each PO, along with demographic information (ie, birth date, gender), claims-derived risk score, chronic disease identifiers via the proprietary clinical analysis tool “Impact Intelligence” (Optum, Eden Prairie, Minnesota), and previous 12-month counts of emergency department visits and hospitalizations. This study included only the members from the monthly lists that were accepted by the POs into the PDCM pilot; a member was accepted when 3 criteria were met (

). Accepted members did not necessarily receive care management, but were eligible to receive care management through their PCP’s practice. Administrative and/or data analytic staff from the POs were responsible for collecting and reporting monthly care management activity data from the practices for their accepted populations.

Quantitative Data Collection and Analysis

We calculated target and engagement rates for each PO’s accepted populations based on reported encoun-ters with care managers (Table 1). The target rate was the number of accepted members that had an outreach attempt, an outreach encounter, or a care management encounter divided by the number of accepted members. The engagement rate was the number of members that had at least 1 care manager encounter divided by the number of targeted members. Two POs each employed 2 different targeting methods; each was treated as a subgroup in analyses. We excluded from the analyses accepted patients who did not remain on the eligible patient lists for at least 2 months after their acceptance date (n = 284, or 4%).

	Health plan members accepted into the PDCM were removed from the plan’s disease management targeting process; therefore, in order to compare the plan’s targeting mechanism with the POs’ methods, the plan’s targeting criteria were retrospectively applied to the claims of the accepted population to identify which members would have met the plan’s targeting criteria had they not been accepted into the PDCM. Thus, among the accepted population, patients either: 1) met PO/practice-based targeting criteria, 2) met plan-based targeting criteria, 3) met both sets of targeting criteria, or 4) were not targeted. We performed 

 tests to compare patient characteristics and target rates of the POs with those of the health plan. Since the pilot patients were managed by the POs rather than the plan, we compared PO engagement rates with the plan’s overall estimated engagement rate of members not in the PDCM.

This study included data from an extensive qualitative analysis of the care management programs, collected as part of a larger comparative effectiveness study. The research team interviewed PO leaders and staff, and conducted observations at 25 of the 51 practices across all 5 POs, purposefully selected to maximize diversity across practices. Hour-long semistructured interviews with multiple practice members were conducted to elicit detail regarding their care management programs and targeting strategies. Similarly, interviews were held with 2 nurses and the program supervisor for the health plan to obtain information on the plan’s program and targeting method.

	A Likert-type scale score sheet was used to quantify the presence or absence of specified care management features, including the process of accepting patients, the training and background of the care manager(s), how and by whom care management was offered to the patient, and the location, frequency, and topics covered in care management visits. Extensive notes were taken to describe each feature, and a summary report was given to each practice for member-checking.

	All data were transcribed and placed into the qualitative software program ATLAS.ti (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany). Three qualitative researchers coded the data after extensive group review to determine appropriate codes and establish consistency of coding. Quotation reports by POs were produced for the relevant codes and reviewed by the research team (3 coders, plus 3 other team members [including JSH]) to determine the processes of identifying and offering care management for each PO and to corroborate the scaled score items.

Practices were in geographically diverse locations within the Lower Peninsula of Michigan, ranging from rural to urban settings.

 describes the characteristics of the 51 pilot practices and the 6988 accepted patients included in this study. The POs varied in the number and size of participating practices, ranging from solo-physician to large multi-physician practices. The mean age of accepted patients was 52 years, and the majority of patients (54%) had diabetes.

The health plan’s routine targeting process for care management involves setting thresholds that are dynamic and determined, in part, by claims-based algorithms that predict members’ future risk of incurring high costs. This commercially available Impact Intelligence program is used by other health plans for similar purposes and is likely generalizable to those groups. Customer groups contract with the plan to target all, or a percentage of, their higher-risk members in chronic disease groups. Targeted members are placed on a list for outreach by one of the health plan nurses who contacts members via phone. Three phone call attempts and a letter are completed before determining that a patient is unreachable.

	The targeting process varied among POs (Table 3). The care managers in PO B were located centrally at an office site and provided services for 1 or more practices via phone. Conversely, the care managers in POs C and E were embedded within the practices, meaning that they were members of the practice care team and worked directly and collaboratively with the PCPs. POs A and D used a combination of the two. For all POs during the pilot, the list of accepted patients was reviewed by a designated person (usually the care manager, but sometimes a medical assistant or panel manager) and discussed with the practice physician(s), who together identified appropriate patients for care management. In a few cases, the physician provided blanket approval, instructing the care manager to “offer care management to all the patients,” but more often, the physician identified specific patients whom he or she thought would benefit and would be willing to participate.

	Diabetes and asthma were a primary focus of the care management programs, given the prevalence of these conditions in the patient populations. Although generally the programs were similar, there was some variability in the types of activities they emphasized some focused more on care coordination, while others focused more on behavior change. However, detailed information about the scope of the programs was not collected.

Articles

The authors compared targeting strategies and characteristics of chronic disease care management programs delivered by primary care practices with one administered by a large health plan.

Comparison of Provider and Plan-Based Targeting Strategies for Disease Management

 To estimate the effect of telephonic wellness coaching on weight loss in a commercially insured population.

 Self-reported weight was obtained from 2 annual health assessment questionnaires administered during 2008 and 2010. Baseline (T1) information from these questionnaires was used to identify overweight/obese individuals and to determine targets for a 4-call wellness coaching program. Overweight/obese individuals identified at T1 were classified into following groups: (1) targeted for wellness coaching (N = 1448, including 1050 participants and 398 nonparticipants); (2) not targeted for wellness coaching, but targeted for other telephonic wellness care management (WCM) programs (N = 1270); (3) not targeted for any WCM programs (N = 7586). Weight reported on questionnaires a year later (T2) was used to calculate weight change between T1 and T2. Paired t-tests were used to detect significant weight changes over time. Multivariable linear regressions were used to compare weight changes between the groups. Stratified analysis was conducted to determine the effectiveness of telephonic wellness coaching for subgroups based on participants’ selected health goals, intensity of the intervention received and initial stage of change.

The group targeted for wellness coaching reported an average weight change of —0.44 kg (95% confidence interval [CI], –0.76 to –0.16) at T2, significantly more weight loss than reported by the group not targeted for any WCM programs. Participants who started in preparation stage and completed the program reported weight change of –1.43 kg (95% CI, –2.17 to –0.68), highest among program participants.

Small weight loss was observed for obese/individuals targeted for telephonic wellness coaching.

Telephonic wellness coaching programs are popular interventions for a large population because of their versatility and convenience.

		This large retrospective study observed small weight loss among telephonic wellness coaching participants more than 6 months after they finished the program.

		It evaluated factors that impact weight loss among wellness coaching participants, including participants’ initial stage of change, motivation level, and intensity of coaching received. This information can assist decision making of whether to offer telephonic wellness coaching programs to a population. The analysis on successful elements of wellness coaching programs can help compare telephonic wellness coaching programs or improve program design.

Obesity poses a substantial threat to the US health and healthcare system.

 Among adults, higher morbidity in association with being overweight or obese has been observed for hypertension,

coronary heart disease, stroke, respiratory problems, and some types of cancers.

 Epidemiologic studies have shown that mortality increases with body mass index (BMI) values above 30.

 Consequently, obesity has conferred a significant economic burden for the nation.

A wide variety of programs have been developed to address the obesity issue in the United States, including wellness coaching programs.

In wellness coaching programs, wellness coaches assist individuals to establish and work toward health-promoting goals using techniques such as motivational interviewing.

 Telephonic wellness coaching offers the personalized focus and discussion, while avoiding common barriers for on-site consultations such as inadequate transportation or lack of childcare. Telephonic coaching programs have been used to address a variety of health behaviors including smoking, nutrition, and weight.

Evidence for the effectiveness of telephonic coaching on weight loss is limited, with only a few studies having demonstrated a small but positive effect.

 Previous published studies have largely not provided details on participant or intervention factors that may mediate the outcomes of such coaching programs. The goals of this study are to estimate the effect of telephonic coaching on weight loss in a large, commercially insured population and to evaluate how a participant’s initial stage of change, motivation level, and intensity of coaching program received impact weight loss among wellness coaching participants.

This study assesses a health plan-administered telephonic wellness coaching program that consists of 4 telephone calls designed to help participants reduce risk factors through health education and behaviorchange counseling. During the initial telephone contact, health coaches review the top 3 risk factors identified on each participant's health assessment questionnaire. Health coaches also assess each participant's readiness to change, level of motivation to change, and perceived barriers to adopt healthy lifestyle behaviors.

Initial stage of change is determined by asking participants how ready they are to make changes to improve their health on a scale of 1 to 10. If participants respond with a score between 1 and 4 and show characteristics of not considering change, feeling no control, or believing consequences not to be serious, the health coach will classify them as precontemplation. If participants respond with a score between 5 and 7 and have considered the benefits and costs of their health behaviors as well as proposed changes in behaviors, the health coach will classify them as contemplation. For participants who respond with a score between 8 and 10, having experimented with small changes will lead to classification as being in the preparation stage; having demonstrated definitive actions to change will lead to classification as being in the action stage; and having definitive actions over an extended time period will lead to classification as being in the maintenance stage.

After the assessment, health coaches work with each participant to create an action plan that includes personalized health-related goals, actions the person will take, strategies for coping with perceived barriers, and identification of who might support them with their efforts. The action plan is mailed to the program participants. During subsequent calls, the health coach discusses progress, barriers and action items with the participant. The length of each phone call ranges between 10 and 20 minutes.

The health coaches that deliver the intervention can be registered nurses, registered dietitians, exercise physiologists or health educators. Several approaches are taken to ensure quality and consistency of interventions being delivered. First, health coaches are required to have a bachelor's or master's degree with additional training in motivational interviewing and certification such as Chronic Condition Professionals from the Health Sciences Institute. Second, health coaches receive ongoing trainings which utilize cognitive behavioral therapy modules. The trainings include classroom training, 1-on-1 instruction, and mentoring by an established coach. Third, fidelity to the program is assessed through randomly recorded phone conversations between health coaches and participants to ensure that the delivery of the program is consistent across health coaches and follows established policies and procedures.

Employers purchase the wellness coaching program benefit for their employees from the health plan. The eligible employees are required to complete an annual health assessment questionnaire at the beginning of each benefit year in order to receive program-related incentives provided by employer groups. The incentives vary across employers from lower office visit and prescription co-pays to gift cards. Health assessment questionnaire respondents are first prioritized for case management or disease management. Disease management is a nurse-administered, telephonic program that targets patients with 1 of 5 chronic diseases: asthma, ischemic heart disease, chronic obstructive pulmonary disease, diabetes, or chronic heart failure, and focuses on chronic disease selfmanagement. Case management is a nurse-administered, telephonic program that targets patients with complex medical needs and focuses on coordination of care. The prioritizing processes for disease management and case management are separate from the health assessment. For respondents who neither need disease management nor case management but have 3 or more risk factors identified via questionnaire, these respondents will receive outbound calls from an engagement specialist to enroll them in the wellness coaching intervention. Individuals who have few risk factors identified via questionnaire do not receive outreach to participate in the wellness coaching program, but are encouraged to use the health plan’s online health tools to maintain good health.

During the course of the study period, there was a change in the health risk assessment questionnaire administered by the health plan. The University of Michigan Health Risk Appraisal (HRA) was used through May 2009

; afterward the StayWell Health Media Health Assessment (HA) was used.

 This change in health assessment questionnaires reflects a business decision of the health plan and not of the authors, who conducted the evaluation retrospectively. The HRA consists of 47 questions while the HA consists of 150 questions. Common health risks assessed by the 2 questionnaires include body weight, existing medical conditions, physical activity, nutrition, alcohol, smoking, stress, safety belt use, skin protection, absenteeism at work, perceived health, and overall life satisfaction. On each instrument, respondents also report their race/ethnicity, gender, level of education, and motivation to lose weight. Although both questionnaires assess similar health risks, the wording of the questions and answers differ markedly. For example, the HRA asks “Do you have heart problems? (Never/Have Currently/In the Past)” while the corresponding HA question is worded as “Have you been diagnosed with coronary heart disease? (Yes/No).” For this evaluation, we only used questions from the 2 questionnaires that could be reconciled to allow consistent measurement over time.

, 40,222 individuals responded to 2 questionnaires at least 6 months apart from 2008 to 2010 for our pre-post evaluation. The average time between baseline (T1) and follow-up (T2) questionnaires was 385 days (approximately 1.05 years) with a standard deviation of 70 days. BMI was calculated from responses to height and weight questions on these surveys. Overweight was defined as a BMI between 25 kg/m

 and obesity was defined as a BMI of 30 kg/m

 or greater. Among respondents, 16,178 (40%) were considered overweight or obese at baseline. Of the overweight and obese respondents, 5653 (35%) were referred for a wellness care management (WCM) program during the study period between baseline and follow-up; 8922 (55%) did not receive any referral during the study period, and 1603 (10%) received a referral, but outside of the period, and thus were excluded from the analysis. Two percent (2%) of the adults who reported a more than 2-inch difference in their height across the 2 time periods were excluded from study population due to potential data quality concern. Respondents with complete data on weight, height, age, gender, race/ethnicity, level of education, motivation to lose weight, smoking status, and comorbidities were used for the analysis. The final study population was divided into 3 groups. The intervention group consisted of obese or overweight respondents targeted for the telephonic wellness coaching program (N = 1448, including 1050 participants and 398 nonparticipants). The 2 comparison groups were overweight or obese respondents: (1) not targeted for any telephonic WCM program (N = 7586) and (2) not targeted for telephonic wellness coaching, but targeted for other WCM programs, primarily disease management (N = 1270).

Differences in the distribution of demographic characteristics, self-reported chronic disease status, smoking status, and motivation to lose weight at baseline among the study groups were tested using c2 test, while differences in baseline BMI among the groups were tested using ANOVA. Paired t-tests were used to detect whether significant weight changes were reported for each group between baseline and follow-up.

To illustrate the impact of outliers, we reported the 1-year weight change for each group after removing outliers in 2 ways: a) statistically, by calculating upper and lower fences based on mean and standard deviation of the weight changes between T1 and T2 using “outer fence” formula (mean + 4.72* standard deviation),

 and b) by removing 4% of the study population that had more than an 18-kg (40-lb) difference (increase or decrease) in self-reported weight based on health coach suggestions of what constituted a substantial weight change.

The association between self-reported weight change and the wellness coaching program was determined using multivariable linear regression, adjusted for sex, race, education, motivation to lose weight, and comorbidities. P values less than .05 were considered statistically significant. Analyses were performed in SAS 9.2.

In addition to our primary analyses among targeted members, we also conducted stratified analysis among wellness coaching program participants. We examined the average weight change stratified by: a) wellness coaching objective; b) the number of phone calls with the wellness coach; and c) the initial stage of change. Since wellness coaching participants could establish multiple goals upon program initiation, we applied a hierarchy to make these categories mutually exclusive. The hierarchy was: weight loss > physical activity > nutrition > smoking > others. Due to sample size concerns, the intensity of intervention (ie, number of phone calls the participant received) was dichotomized into 1 to 3 calls and 4 or more calls. Although the program is designed to consist of 4 calls, on rare occasions participants (<1%) may have had more than 4 calls with a health coach if additional coaching was requested by the participant. Participants who received 4 or more phone calls with health coaches were considered program completers. We used a paired t-test to determine whether weight changes were significant for each group between baseline and follow-up, with 95% confidence intervals (CIs) for weight changes were reported.

Demographic Characteristics for Study Population 

shows the characteristics of the study population. This population was well educated, with 70% of them having had at least some college education. The majority of the population was Caucasian; 55% of the population was male. The distributions of gender, race/ethnicity, and education were not different among the groups. More than 70% of respondents reported having motivation to lose weight in all study groups.

The average age was 45 years or above for all 3 groups, with the group targeted for other WCM programs having the highest average age (50 years). The groups were significantly different in their smoking and chronic disease status. Respondents targeted for other WCM programs had consistently higher rates of diabetes (40%) and asthma (27%) compared with respondents targeted for wellness coaching (12% and 9%, respectively). The group targeted for wellness coaching program had the highest prevalence of ever smoking (smoking sometime during their lifetime, may or may not smoke currently). As expected, the group not targeted for any programs had the fewest risk factors. Overall, the group targeted for wellness coaching was more similar to the group not targeted for any program than to the group targeted for other WCM programs.

Average Weight Change for Intervention and Control Groups 

 provides the changes in self-reported weight between baseline and follow-up at 1 year by group, removing outliers using the 2 methodologies. After excluding 4% of individuals who reported a more than 18-kg (40-lb) weight change between baseline and follow-up, the average weight change was —0.44 kg (95% CI, –0.76 to –0.16) among respondents targeted for wellness coaching and –0.74 kg (95% CI, –1.06 to –0.15) specifically among wellness coaching participants. There was no statistically significant weight loss reported by either comparison group.

 shows the comparison of weight changes among the groups from the multivariable model. After excluding 4% of respondents who reported more than 18 kg (40 lb) weight changes, respondents targeted for wellness coaching reported —0.59 kg (95% CI, –0.88 to –0.30) more weight loss than respondents not targeted for any program. The unadjusted model and the full model adjusting for age, gender, education, and self-reported comorbidities yielded similar coefficient estimates for being targeted for wellness coaching. The respondents targeted for wellness coaching reported a 0.28-kg greater weight loss than the respondents targeted for other wellness programs, but the difference was not statistically significant.

Stratified Analysis Among Wellness Coaching Program Participants

shows the results of average weight change stratified by participants’ goals, number of phone calls between the health coach and the participants, and the participants’ initial stage of changes. Based on the results of the paired t-test, individuals who set goals of weight loss and physical activity benefited most by losing 1.51 kg (

= .01), respectively with no significant weight change reported for participants that set other goals. Participants who started in the preparation stage report an average weight change of —1.43 kg (95% CI, –2.17 to –0.68) if they completed the 4 phone calls.

Overweight or obese health assessment respondents targeted for wellness coaching reported an average of .44 kg weight loss, .59 kg more weight loss than health assessment respondents not targeted for any wellness care management programs. Among wellness coaching participants, we observed that individuals who set goals of weight loss and physical activity reported significant weight loss while individuals who chose other health goals did not. Participants who started wellness coaching in the preparation stage and completed the 4-call program reported the most weight loss (—1.43 kg).

The strengths of this study are the large sample size and collection of program participation information among program participants that allowed stratified analysis using factors such as initial stage of change, wellness goals set, and program completion. It is interesting that among participants who began the program in the preparation stage, those who completed all 4 calls reported the most weight loss while those who did not complete the 4 calls reported no significant weight loss. Meanwhile, the weight loss reported by participants who started in the action stage did not differ by whether they completed the 4-call intervention or not. This suggests that if health coaches are able to increase the motivation preparation stage, and provide them necessary tools to act on, these individuals may benefit from the program while participants in other stages may realize little benefit from the program as it is currently designed. Interventions may need greater customization according to the participants’ initial stage of change to maximize program benefits for all participants.

These results should be interpreted conservatively, since this is a retrospective study with limited information collected for the study population. There are many additional unobserved factors could have had an impact on weight loss among program participants. For example, motivated participants might enroll in wellness or weight loss programs in addition to the employer-sponsored and health plan-administered wellness coaching programs. It is unclear whether these unobserved factors were distributed differentially among the groups and whether this impacts the comparison among the 3 groups. Despite the large sample size, another limitation of this study is the generalizability of the findings. Our study population includes commercially insured individuals with education levels higher than those of the general population. The findings may not be generalizable to minority, younger, or less educated populations. Using selfreported weight is another limitation of this study.

The respondents targeted for telephonic wellness coaching reported a small but significant weight loss. It is unclear whether weight loss can be sustained beyond 1 year and whether the small weight loss observed has meaningful longterm health benefits. Sustaining weight loss is a persistent issue in weight loss programs.

We only identified 1 published study that followed telephonic weight loss program participants for up to 2 years, and that study reported no significant weight loss among telephonic coaching participants at 2 years compared with participants who received a mail intervention or usual care.

 It is worth noting that in this HealthPartners clinical trial, participants who received either phone, mail, or usual care were all highly motivated volunteers who responded to mail or a clinic poster about the trial. Because a major technique in telephonic wellness coaching is motivational interviewing, the benefit of intervention may be limited to participants who are already highly motivated. As shown in this study, wellness coaching participants who started in the preparation stage benefited significantly from the program. Participants who were already in the action stage reported weight loss, but showed no additional benefit from completing the program. Additional studies with different populations and additional settings are needed to fully address the impacts of telephonic wellness coaching on both short-term and long-term weight loss.

Research suggests that a moderate amount of weight loss has potential benefits for obese patients.

 But the amount of weight loss observed in this telephonic wellness coaching program is less than 1% of total body weight and among a healthier population. Future studies on telephonic wellness coaching and weight loss may need to also report details on nutritional and physical activity components of the programs that could be associated with weight loss and the sustainability of these behaviors. If the lifestyle changes adopted through wellness coaching result in a sustainable, small amount of weight loss, this may improve health outcomes in the long term.

Clinical Epidemiology and Biostatistics, Health Care Value, Blue Cross Blue Shield of Michigan (BCBSM).

Drs Tao, Paustian, and El Reda all report being employed by BCBSM. Mr Rangarajan and Dr Wasilevich report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (MT, KR, MLP, EAW, DKER); acquisition of data (MT, DKER); analysis and interpretation of data (MT, KR, MLP, DKER); drafting of the manuscript (MT, EAW, DKER); critical revision of the manuscript for important intellectual content (MT, MLP, EAW, DKER); statistical analysis (MT, KR); and supervision (MT, DKER).

Small weight loss was reported by overweight/obese individuals targeted for telephonic wellness coaching in this large retrospective study using pre-post design.

Min Tao, PhD

Articles

Comparison of Provider and Plan-Based Targeting Strategies for Disease Management

Dialing In: Effect of Telephonic Wellness Coaching on Weight Loss