Precision Lifestyle Medicine: A New Frontier in the Science of Behavior Change and Population Health

Given their association with varying health risks, lifestyle-related behaviors are essential to consider in population-level disease prevention. Health insurance claims are a key source of information for population health analytics, but the availability of lifestyle information within claims data is unknown. Our goal was to assess the availability and prevalence of data items that describe lifestyle behaviors across several domains within a large U.S. claims database.

We conducted a retrospective, descriptive analysis to determine the availability of the following claims-derived lifestyle domains: nutrition, eating habits, physical activity, weight status, emotional wellness, sleep, tobacco use, and substance use. To define these domains, we applied a serial review process with three physicians to identify relevant diagnosis and procedure codes within claims for each domain. We used enrollment files and medical claims from a large national U.S. health plan to identify lifestyle relevant codes filed between 2016 and 2020. We calculated the annual prevalence of each claims-derived lifestyle domain and the proportion of patients by count within each domain.

Approximately half of all members within the sample had claims information that identified at least one lifestyle domain (2016 = 41.9%; 2017 = 46.1%; 2018 = 49.6%; 2019 = 52.5%; 2020 = 50.6% of patients). Most commonly identified domains were weight status (19.9–30.7% across years), nutrition (13.3–17.8%), and tobacco use (7.9–9.8%).

Our study demonstrates the feasibility of using claims data to identify key lifestyle behaviors. Additional research is needed to confirm the accuracy and validity of our approach and determine its use in population-level disease prevention.

This mixed method analysis examined fidelity of the motivational interviewing (MI) delivered during the 5Minutes4Myself wellness program for caregivers of children with Autism Spectrum Disorders.

Coaches used a manualized MI-approach to collaboratively design participants’ individualized wellness programs, and then used it in monthly coaching sessions to support goal pursuit. Audio-recorded consultations and post-participation focus groups were transcribed verbatim. Consultation transcripts were rated for MI adherence using Motivational Interviewing Treatment Integrity Code. Focus group data was coded by the team to identify conceptual categories.

Eighty-seven percent of consultations achieved a beginning (competent) level expert-derived MI standard. Caregivers noted that coaches’ MI approach evoked deep emotions and understandings about their lives related to wellness goals, allowed for a permissive flexibility in goal pursuit, and fostered supportive accountability.

MI was used with high levels of fidelity. Participants described the MI approach as more productive, precise, and useful in addressing their needs compared to other available interventions.

Human gut microbiota varies in richness and functional capacity. Microbiota profiles are promising biomarkers in health and disease risk management, and prognostic biomarkers in chronic and communicable disease onset and progression. Gut microflora is modulated through tailored, personalized dietary and nutritional regimes, based on an individual's characteristics. Interventional strategies inducing changes in gut microflora, could cluster subjects to either responder or non-responder group. Further studies should elucidate the role of microbiota in disease etiology, especially diseases associated with inflammation burden such as non-alcoholic fatty liver disease, or immunological disorders such as Hashimoto thyroiditis.

Because the metabolome reflects the overall outcomes of the complex network of interactions in an organism, metabolomics studies have become essential tools for the scientific community. In this sense, it is known that homeostasis is constantly challenged by several factors, such as nutrition, diseases and stress, and that the final outcome reflects their ability to properly maintain the robustness of homeostatic. Hence, metabolomics studies allow for a better understanding of the mechanisms and pathways involved in these processes and of biological problems. In this chapter, an updated review of the use of metabolomics in the evaluation of the response to metabolic disturbances, including chronic diseases, nutrition, physical exercise and biological rhythms, is presented.

Negative views of aging (NVOA), low self-efficacy beliefs, and poor goal planning skills represent risk factors that undermine adults' motivation to engage in physical activity (PA). Targeting these three risk factors may motivate adults to become physically active.

To assess the efficacy of Aging^PLUS, a 4-week educational program that explicitly targets NVOA, low self-efficacy beliefs, and poor goal planning skills compared to a 4-week health education program. The study also examines the role of NVOA, self-efficacy beliefs, and goal planning as the mechanisms underlying change in PA.

This randomized controlled trial (RCT) utilizes the experimental medicine approach to assess change in PA as a function of modifying three risk factors. The RCT recruitment target includes 288 mostly sedentary adults ranging in age from 45 to 75 years.

Eligible middle-aged and older adults are recruited through community sources. Participants are randomized to either the Aging^PLUS or the control group. Participants in both groups are enrolled in the trial for 8 months total, with four assessment points: Baseline (pre-test), Week 4 (immediate post-test), Week 8 (delayed post-test), and Month 6 (long-term follow-up). The intervention takes place over 4 consecutive weeks with 2-h sessions each week. PA engagement is the primary outcome variable. Positive changes in NVOA, self-efficacy beliefs, and goal planning are the intervention targets and hypothesized mediators of increases in PA.

By utilizing a multi-component approach and targeting a cluster of psychological mechanisms, the Aging^PLUS program implements the experimental medicine approach to health behavior change.

Mobile health helps providers offer accessible, affordable, tailored behavior change interventions. However, research assessing mobile health interventions may feature methodologic shortcomings and poor reporting. This review aims to summarize the characteristics, methods, and intervention reporting of RCTs evaluating mobile health behavior change interventions.

This was a methodologic systematic review of RCTs assessing mobile health behavior change interventions published in PubMed from January 1, 2014 to January 1, 2018, in journals with the upper half of Impact Factors (Clarivate Analytics). Three reviewers independently extracted sample characteristics. Primary outcomes were classified as patient-important or not using definitions from the literature. Any non–patient-important outcomes were then reclassified by a panel of 3 patients. Intervention reporting was assessed by the mobile health Evidence Reporting and Assessment checklist. Data were analyzed in December 2018.

Most of the 231 included RCTs assessed text messaging (51%) or smartphone app (28%) interventions aiming to change nutrition and physical activity (36%) or treatment adherence (25%). Only 8% of RCTs had a patient-important primary outcome, follow-up of ≥6 months, and intent-to-treat analysis. Most primary outcomes were behavioral measures (60%). Follow-up was <3 months in 29% of RCTs. Regarding reporting, 12 of the 16 checklist items were reported in less than half of RCTs (e.g., usability/content testing, 32%; data security, 13%).

Reports of RCTs assessing mobile health behavior change interventions lack information that would be useful for providers, including reporting of long-term intervention impact on patient-important primary outcomes and information needed for intervention replicability.