A Novel and Comprehensive Wellness Assessment for Lifestyle‐Based Interventions

ABSTRACT

Objective

While weight status and clinical laboratory measures are important in assessing obesity‐related disease severity and chronic disease risk, including a broader range of emotional, psychosocial, and behavioral factors would provide greater context of an individual's overall state of wellness and could be used to better guide treatment decisions. The purpose of this research was to develop a comprehensive Lifestyle Wellness assessment for use in lifestyle‐based wellness interventions and programs.

Methods

A cross‐sectional exploratory factor analysis (EFA) was conducted using baseline data from N = 138 adults participating in behavioral weight loss trials. An unweighted least squares extraction method with oblique rotation was used. Twenty‐one putative measures spanning constructs related to metabolic health, emotional health/wellbeing, body weight and composition, diet quality, and fitness were analyzed for retention.

Results

Mean body mass index (BMI) was 38.0 ± 6.6 kg/m², mean age was 57.3 ± 11.1 years, and 77.5% of participants were female. The EFA produced a five‐factor model with 13 items that explained 80.3% of the variance. The retained factors included: (1) Psychosocial State: mindfulness, resilience, quality of life, and happiness; (2) Blood Pressure State: systolic and diastolic blood pressure; (3) Lipid State: total cholesterol and LDL‐cholesterol; (4) Fitness State: grip strength, jump height, and percent body fat; and (5) Body State: BMI and waist circumference.

Conclusions

Lifestyle Wellness is a comprehensive assessment that enables innovative wellness‐related research such as metabolically healthy obese phenotypes and weight‐neutral interventions. Future research should include investigations in additional populations with greater age, sex/gender, and body size diversity.

The purpose of this research was to develop a comprehensive Lifestyle Wellness assessment for use in lifestyle‐based wellness interventions and programs. The exploratory factor analysis produced a five‐factor model with 13 items that explained 80.3% of the variance. Lifestyle Wellness is a comprehensive assessment that enables innovative wellness‐related research such as metabolically healthy obese phenotypes and weight‐neutral interventions.

1. Introduction

Obesity is one of the most detrimental diseases routinely seen in primary care and there is clear evidence that it is causally related to heart disease, type 2 diabetes, stroke, and some cancers [1]. Weight loss of 5% is generally accepted as the minimum threshold for clinical significance, but the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) recommend 10%–15% weight loss for those with comorbid conditions [2]. While weight loss is undoubtedly an important aspect of obesity treatment, evidence indicates that patients also desire to focus on improving other health aspects including appearance, body composition, strength, endurance, flexibility, metabolic health, perceived body image, eating habits, life satisfaction, mood, and mindset [3, 4, 5, 6]. Similarly, while anthropometric and clinical laboratory measures are important in assessing weight‐related disease severity, measuring the emotional, psychological, and social implications of obesity would provide a greater context of a patient's disease burden and could be used to better guide treatment decisions [7]. The Canadian Clinical Practice Guidelines for Obesity also recommend patient‐centered care approaches that emphasize contextual outcomes for treatment in addition to achieving weight loss [8]. Despite official recommendations and patient preferences to expand treatment outcomes, obesity's medical management model remains nearly exclusively focused on weight loss.

While clinicians have primarily used body weight and metabolic values to gauge the success of an obesity treatment, research shows that patient satisfaction with weight loss goes beyond the expected weight reduction. Ingels, Hansell, and Zizzi conducted a qualitative analysis of participant expectations and preferences and discovered that participants preferred to measure success of their obesity treatment intervention by improvements in body composition, strength, endurance, flexibility, metabolic health, body image, eating habits, life satisfaction, mood, and mindset [3]. Foster et al. found greater than expected perceived benefits in areas such as overall health, strength, fitness, social life satisfaction, competence, social comfort, stress, depression, and self‐confidence even among participants who lost much less weight than they wanted [4]. Another study found that participants were motivated by improvements in health, mobility, well‐being, and body appearance in addition to wanting weight loss [9]. Participants also reported that they felt primary care providers were the best people to help them lose weight, but the majority thought that interventions should not focus on weight loss alone because a weight‐centric approach to treatment was not perceived to be empowering [9]. These studies demonstrate that people want to glean benefits from obesity treatments in addition to weight loss.

It is evident that clinicians may benefit from a comprehensive understanding of the multifactorial benefits of weight loss treatments to effectively capture patient satisfaction in their assessments. Numerous health and wellness assessments have been used in clinical care and include a range of health measures [10]. This underscores their demand as many practitioners recognize obesity as a multifactorial disease that increases risks of several comorbidities and requires a multidisciplinary care team to treat [2]. While endocrinologists or obesity medicine specialists are the primary disciplines to treat obesity, psychologists have also played a central‐ and underutilized‐ role, especially in strengthening the field's understanding of the behavioral and psychosocial aspects of obesity [11, 12]. For example, the Weight and Lifestyle Inventory (WALI) was created by psychologists to identify behavioral and psychosocial (i.e., subjective) baseline characteristics that may predict obesity treatment outcomes [13, 14]. However, it may be most useful for an assessment to also objectively measure health to track changes overtime and determine treatment success. However, existing assessments are either incomprehensive and only assess subjective [13, 15, 16] or objective measures of health [2], are too time consuming to employ during primary care visits [17, 18, 19], are expensive or inaccessible to implement [17, 18, 19, 20], demonstrate poor validity and reliability [21], or were never validated for clinical use [16, 22, 23, 24]. Therefore, a scalable and comprehensive assessment that captures holistic health measures is needed to (1) broaden the scope of obesity treatment to include patient‐centered outcomes, (2) provide greater context of patient disease burden and (3) help guide treatment decisions by providers.

This study demonstrates the efforts to develop a Lifestyle Wellness Assessment (LWA) that incorporates research‐supported measures associated with overall weight reduction. The LWA includes constructs on individual quality of life, life satisfaction, happiness, dietary behaviors, physical fitness, body composition, and mindset. The study team first decided which constructs to test for inclusion in the LWA based on those that could be feasibly implemented in primary care. The measures were subsequently included in two clinical weight loss trials for data collection. All dimensions were scored and aggregated to an overall wellness score. Scoring procedures are described in the Supporting Information and Discussion. The purpose of this research is to develop a scalable and reliable holistic health assessment that is more inclusive of patient preferences for obesity treatment outcomes. We hypothesized that LWA items would load on six factors: metabolic state, life (emotional) state, diet/nutrition state, fitness state, body state, and mind state.

2. Methods

2.1. Participants

Convenience samples totaling 138 adults from two behavioral weight loss interventions (NCT04014296, NCT04745572) were used for analyses. Eligible participants in the first weight loss study (n = 55) were ≥ 50 years old, postmenopausal if female (≥ 1 year since last menstrual period), had a body mass index (BMI) ≥ 30 kg/m² and were not taking insulin if they had type 2 diabetes. Participants in the second study (n = 83) were eligible if they had prediabetes (A1c ≥ 5.7% and/or fasting glucose ≥ 100 mg/dL), were between 18 and 75 years old and had a BMI ≥ 27 kg/m² [25]. For both studies, participants were required to have no use or stable use (≥ 3 months on same dosage) of medications affecting body weight and no pacemaker or other battery‐operated implant. Putative measures for the LWA were performed at baseline study visits. The in‐person testing included the Metabolic State, Fitness State, and Body State measures (described below) and took approximately 20 min to administer Participants were instructed to fast for at least eight hours and stay hydrated while refraining from moderate‐to‐vigorous physical activity for 24‐h prior to testing. The Life, Nutrition, and Mind State measures were assessed by online questionnaires, which were sent via REDCap and completed within 1 week after in‐person testing. The questionnaires took approximately 20 min to complete. The University of Alabama at Birmingham (UAB) Biomedical Institutional Review Board approved the clinical trials and this analysis.

2.2. Demographics

Demographic information was collected via REDCap and are presented in Table 1. These data were collected at baseline through a self‐report questionnaire.

TABLE 1.

Participant characteristics (n = 138).

Characteristic	Mean ± SD or n (%)
Age	57.3 ± 11.1
Race
Non‐Hispanic White	61 (44.2)
Non‐Hispanic Black	69 (50.0)
American Indian or Alaska Native	1 (0.7)
Asian	3 (2.2)
Other	4 (2.9)
Sex
Male	31 (22.5)
Female	107 (77.5)
Height, cm	166.1 ± 8.1
Weight, kg	105.9 ± 22.0
BMI, kg/m2	38.0 ± 6.6
25th percentile	33.1
50th percentile	36.7
75th percentile	42.4
WC, cm	117.5 ± 15.1

Note: Characteristics are shown for participants at baseline. Height, weight, and waist circumference were measured by trained personnel.

Abbreviations: BMI, body mass index; WC, waist circumference.

2.3. Lifestyle Wellness Assessment

The LWA included six wellness dimensions as shown in Table 2. Sum scores from the questionnaires in the LWA were used as items in the exploratory factor analysis (EFA) instead of their individual questions because (1) most of the questionnaires were previously validated (with the exception of the Growth and Voyager Mindset Questionnaire) and the intent was to develop a multi‐dimensional wellness assessment comprising constructs from these validated measures (2) with a relatively small sample size, factor recovery is better with at least five cases for every one item [26]. If individual questions had been used, there would have been only one case for each item compared to five cases when using sum scores. Details about each LWA assessment used in the EFA can be found in Supporting Information. Altogether, the in‐person tests and the online questionnaires took approximately 40 min to complete.

TABLE 2.

Items included in the EFA and their hypothesized domains.

Lifestyle wellness assessment
Metabolic state	Total cholesterol
	HDL‐cholesterol
	LDL‐cholesterol
	Hemoglobin A1c
	Systolic blood pressure
	Diastolic blood pressure
Life state	Quality of life scale
	Oxford happiness questionnaire
	Satisfaction with life scale
Nutrition state	Rapid eating assessment for participants short version
Fitness state	3‐Minute step test
	Plank test
	Vertical jump test
	Sit and reach test
	Handgrip dynamometry
Body state	Body fat percent
	Waist circumference
	Body Mass index
Mind state	Short‐form five facet mindfulness questionnaire
	Brief resilience scale
	Growth and voyager mindset questionnaire

2.3.1. Metabolic State

Point of care (POC) Fingerstick Samples and Analyses: The Cholestech LDX (San Diego, CA) device was used to assess glucose, total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), calculated low‐density lipoprotein cholesterol (LDL‐C), and triglycerides (TG). Glucose and TG were not included in the primary EFA model because of their sensitivity to fasting versus fed states. Hemoglobin A1c (A1c), was collected and measured using an Alere Affinion analyzer.

Blood Pressure: Blood pressure was measured using a Welch Allyn 71WT‐B Connex Spot automated blood pressure monitor.

2.3.2. Life State

Quality of Life Scale (QoLS): This 16‐item questionnaire uses a seven‐point Likert scale to assess quality of life (QoL).

Oxford Happiness Questionnaire (OHQ): The OHQ contains 29 items utilizing a six‐point Likert scale to measure psychological well‐being.

Satisfaction with Life Scale (SWLS): This scale contains five items with a seven‐point Likert scale to assess people's satisfaction with life.

2.3.3. Nutrition State

Rapid Eating Assessment for Participants Short Version (REAP‐S): This 13‐question survey assesses participant diet quality.

2.3.4. Fitness State

3‐Minute Step Test: Aerobic capacity was measured using the YMCA's 3‐min Step Test (3MST) protocol to estimate maximal oxygen uptake as a measure of cardiovascular fitness.

Muscular Endurance (Plank Test): Participants held a plank position as a test of muscular endurance.

Muscular Power (Vertical Jump): Vertical jump was used as an indicator of mobility and functional capacity.

Flexibility (Sit and Reach): This test measured flexibility of the lower back and hamstring muscles.

Handgrip Strength: Handgrip strength was used to measure muscular strength and maximal force of the forearm.

2.3.5. Body State

InBody S10 (Cerritos, CA) Body Composition Analyzer: Touch‐type electrodes were clipped to participants' thumbs, middle fingers, and ankles to measure body fat percent (BF%).

Waist Circumference: Waist circumference was measured in duplicate at the boarder of the iliac crest to the nearest 0.5 cm.

BMI: To calculate BMI (kg/m²), body weight was obtained from a platform scale and measured to the nearest 10th of a kg using either DETECTO BRW1000, DETECTO, Webb City, MO, or Health O Meter digital scale. Height was measured to the nearest 0.5 cm using a stadiometer.

2.3.6. Mind State

Short‐Form Five Facet Mindfulness Questionnaire (SF‐FFMQ): The SF‐FFMQ was used to measure mindfulness on a five‐point Likert scale.

Brief Resilience Scale (BRS): The BRS was used to measure resilience using a five‐point Likert scale.

Growth and Voyager Mindset Questionnaire (GVMQ): The GVMQ was used to measure cognitive orientation on a seven‐point Likert scale.

2.4. Data Analysis

An EFA was conducted using participant (n = 138) Lifestyle Wellness measures to determine the factor structure of the proposed LWA and identify which items should be retained for final analyses. An unweighted least squares extraction method was used because it is acceptable if communalities are high and when few factors are expected to be extracted [27]. An oblique rotation method (direct oblimin) with Kaiser Normalization was used for rotation to allow variables to be correlated. Twenty‐one items were included in the EFA. Model fit statistics, residuals of the reproduced correlations, and communalities were analyzed and interpreted. Kaiser's criterion was used to determine the number of factors to retain (factors with eigenvalues > 1), and scree plots were analyzed for confirmation. To produce the most parsimonious model with moderate to high item‐to‐factor correlations, the study team decided to consider the removal of items with coefficients < 0.6 [16]. Items with the lowest loadings (or cross loadings) were removed first and then items were continuously removed until the pattern matrix provided a parsimonious model where all item correlations were ≥ 0.6. Cases were excluded pairwise to handle missing data. Cronbach's alpha (α) was used to measure reliability and values > 0.60 were considered satisfactory [28]. Item total correlations were used to measure internal consistency and values > 0.20 were considered satisfactory [29]. Statistical analyses were performed using SPSS version 29.0 (Chicago, IL).

3. Results

3.1. Exploratory Factor Analysis

The EFA included 21 items from n = 138 adults (57.3 ± 11.1 years; 77.5% female) with obesity (BMI 38.0 ± 6.6 kg/m²). The scree plot from the first round of dimension reduction suggested that there were seven factors with eigenvalues > 1.0 that explained 70.2% of the variance of the interindividual differences of each factor. Items were removed one at a time if they did not load under a factor (coefficients < 0.6) and subsequently replaced back into the model so that every combination of items was realized. Items were completely removed if their exclusion produced a more parsimonious model with greater explained variance. The following items were removed in order and resulted in a five‐factor model explaining 80.3% of the variance in the sample (Table 3): A1c, REAP‐S, sit and reach, GVMQ, HDL‐C, SWLS, 3MST, and plank. Retained questionnaires from the Life State and Mind State domains loaded together on factor 1 (SF‐FFMQ, BRS, QOLS, AND OHQ) were collectively named Psychosocial State. Grip strength and jump height from the Fitness State domain and BF% from the Body State domain were loaded on factor 2, which was named Fitness State. SBP and DBP from the Metabolic State domain loaded on factor 3 and TC and LDL‐C (also from the Metabolic State domain) loaded on factor 4. These factors will be treated as distinct subfactors of Metabolic State and named Blood Pressure State and Lipid State, respectively. BMI and WC from the Body State domain loaded on factor 5 and will retain the name.

TABLE 3.

Pattern matrix of the final five‐factor solution.

Factor	1	2	3	4	5
SF‐FFMQ	0.719
BRS	0.644
QOLS	0.678	0.152		−0.123
OHQ	0.901
TC				−0.980
LDL‐C				−0.911
SBP			0.962
DBP			0.749
Jump		−0.704			−0.140
BF% S10		0.700			0.225
WC					0.876
Grip		−0.805	0.134		0.358
BMI		0.192			0.869

Note: Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 12 iterations. Bolded coefficients represent significant factor loadings that were retained for the final assessment.

Abbreviations: BF% S10, body fat percent from InBody S10 device; BMI, body mass index; BRS, Brief Resilience Scale; DBP, diastolic blood pressure; Jump, jump height test; Grip, handgrip dynamometry; LDL‐C, low‐density lipoprotein cholesterol; OHS, Oxford Happiness Scale; QOLS, Quality of Life Scale; SBP, systolic blood pressure; SF‐FFMQ, Short‐Form Five Facet Mindfulness Questionnaire; TC, total cholesterol; WC, waist circumference.

3.2. Item Total Correlations

Internal consistency (α) and item‐total correlations were calculated to evaluate the final scales for each factor. The range of item‐total correlations were satisfactory for the psychosocial factor (0.56–0.77), the blood pressure factor (0.71), the lipid factor (0.89), and the body state factor (0.60), but were lower for the fitness factor (grip strength: −0.24, jump height: 0.36, and BF%: −0.47). Reliability (α) was also satisfactory for the psychosocial measures (> 0.71) but low for two of the fitness measures (grip strength: −1.38 and BF%: 0.26; jump height: −0.70). For more information on the specific scales, please contact the authors.

4. Discussion

The LWA provides a holistic assessment of wellness that comprises five dimensions: Psychosocial State, Fitness State, Blood Pressure State, Lipid State, and Body State. Compared to previously established wellness assessments, the LWA comprises both subjective and objective measures of wellness, permitting a more comprehensive evaluation of patients with obesity. It does not take long to administer (approximately 20 min) and could be feasibly implemented in primary care settings, which is the initial planned setting for clinical implementation.

Overall, the LWA demonstrated good internal consistency and therefore can discriminate between various levels of wellness. Contrary to our initial hypotheses, the final EFA model revealed: (1) five wellness dimensions, (2) BF% factor loaded with measures of Fitness State instead of Body State, and (3) measures of Metabolic State were not unidimensional and split into two factors. The participants in this study were overweight or obese, but additional studies are ongoing using the LWA to evaluate wellness in populations without obesity and feasibility of implementation in primary care.

The POC devices used in the present study provided results for glucose and TG, which were collected in a research setting under fasting‐state conditions. However, fasting blood samples are not always feasible in primary care clinical settings, which is intended to be the principal clinical implementation setting for LWA. An exploratory EFA including fasting‐state measures of glucose and TG was conducted to determine whether their inclusion substantively improved the model and justified the additional requirement of obtaining a fasting‐state blood sample. This EFA resulted in a six‐factor solution explaining 81.1% of the variance where fasting glucose and A1c factor loaded together. Considering the feasibility limitations of obtaining fasting glucose and the observation that including the glucose/A1c factor did not result in a meaningfully greater explained variance (80.3% vs. 81.1%), we consider the five‐factor solution to be the primary model for the LWA. Despite the decision to remove blood glucose measures from this final assessment, blood glucose and A1c levels are highly relevant to obesity treatment, and these measures should still be collected to inform appropriate obesity care, especially in patients with prediabetes or diabetes.

The multidimensionality of Metabolic and Body State indicates the presence of two latent constructs driving their distinction in this sample of people with obesity. These constructs are often conflated because obesity (i.e., a high BF%, BMI, and/or WC) and metabolic disease are strongly related. A primary example of this conflation is in the diagnosis of metabolic syndrome, which includes WC as a primary component and previously as an obligatory component by some organizations [30]. However, obesity can exist without metabolic dysfunction and vice versa. A well‐known example is the metabolically healthy obesity (MHO) phenotype in which metabolic disease (especially high fasting glucose, dyslipidemia, and hypertension) is absent despite having a BMI ≥ 30 kg/m² [31]. It is also possible to develop type 2 diabetes [32] or cardiovascular disease [33, 34] while having a normal BF%, BMI, and/or WC, a condition sometimes referenced as the metabolically unhealthy normal weight phenotype [35]. The finding that BF% loaded with jump height and grip strength in the LWA further reinforces the need for greater consideration of the complex relationships between body size, body composition, and health/wellness. The current observations that lower BF% factor loads with higher jump height and greater grip strength are consistent with previous findings that lower BF% is associated with fitness, regardless of BMI or WC [36]. In this context, BF% could be used as a measure to gauge fitness level rather than body size, as it has been used traditionally.

The distinction of Metabolic State and Body State in the LWA enables two areas of future wellness‐related research and treatment. In the context of obesity treatment, LWA‐based interventions could compare the effectiveness of weight‐focused (i.e., weight loss) and weight‐neutral (i.e., do not emphasize weight change) approaches for improving metabolic health in people with obesity and metabolic syndrome. Such weight‐neutral approaches and concepts related to body positivity are promoted by organizations such as the Association for Size Diversity and Health, which holds the trademark for Health at Every Size (HAES). Weight‐neutral or HAES interventions have been shown to improve health‐related quality of life parameters [37] and cardiovascular risk factors in the absence of weight loss [37, 38]. Secondly, the distinction of Metabolic State from Body State potentially broadens the scope and reach of wellness‐related lifestyle interventions to be more inclusive of people with cardiometabolic abnormalities but without an elevated BMI and/or WC as discussed above. The common conflation of obesity with metabolic disease and similarly the conflation of “dieting” with weight loss may leave these individuals feeling as if traditional lifestyle‐based interventions are not designed for them because they do not perceive a need to lose weight. In both cases, the LWA and LWA‐based interventions would enable patients and their healthcare providers to develop individualized treatment plans that target either Body State or Metabolic State individually, sequentially, or simultaneously depending on patient preferences and needs.

Interestingly, items from the Metabolic State domain were separated into two subfactors comprising lipid (TC and LDL‐C) and blood pressure‐focused (SBP and DBP) factors. Despite their common co‐occurrence, results of this EFA suggest that dyslipidemia and high blood pressure are distinguishable constructs of overall metabolic health. This observation supports a larger body of research suggesting that the components of metabolic syndrome (fasting glucose, WC, SBP, TG, and HDL‐C) may cluster within distinct subgroups according to race/ethnicity, age, and sex/gender differences [39]. Another study conducted a confirmatory factor analysis (CFA) of metabolic syndrome components and found that loadings differed by racial/ethnic and gender subgroups [40]. Subsequently, the authors used these data to develop the Metabolic Syndrome Severity Score (MSSS) as a tool for predicting and diagnosing metabolic syndrome that has recently been tested in clinical care with promising feasibility [41]. The multidimensionality of lipid and blood pressure‐focused factors observed in the present study seems to support the idea of metabolic syndrome subgroups. Similar to the MSSS, the LWA could be used to gauge levels of wellness and make therapeutic decisions.

We recognize that there may be other wellness dimensions relevant to people with obesity that were not included in the EFA. e.g., sleep is frequently identified as a wellness dimension and changes in sleeping behavior often occur alongside changes in physiological and psychological conditions. Financial wellness is also impacted by health behaviors (i.e., spending money on gym memberships, eating out less) and can also be affected by physical or psychological changes. However, since outpatient obesity treatment typically occurs in primary care, the research team decided to include aspects in the LWA that could be feasibly measured and intervened upon during primary care visits. For example, grip strength, jump height, WC, and BF% could easily be implemented during physician visits, whereas the Psychosocial State questionnaires could be completed by patients while waiting to see their doctor or before/after visits. Other LWA measures (BMI, SBP, and DBP) are already routinely measured in primary care. Scores for each assessment were then calculated and used as a decision‐making tool to guide obesity treatment by physicians. A major strength of this study is its applicability to clinical settings. A sub‐sample of data collection took place in a primary care clinic, which serves as preliminary evidence for the feasibility of implementing the LWA in clinical settings. While it was a data‐driven approach that suggested removing the 3MST, plank, and sit and reach tests, removal of these items also reduced patient burden. While these fitness tests require a baseline level of physical aptitude, the jump test can be safely performed in populations with physical limitations, which has been demonstrated in older adults with sarcopenia or osteoporosis [42, 43]. Grip strength is also a low‐burden and relatively low‐cost assessment that could easily be implemented in clinical settings. Additionally, removing three of the questionnaires (REAP‐S, SWLS, and GVMQ) further reduces patient burden; thus, the total time to administer the LWA was reduced from approximately 40 to 20 min. Removal of the REAP‐S provides an additional level of flexibility in dietary patterns that may be used to improve health and wellbeing as measured by the LWA. For example, some dietary patterns such as a ketogenic diet have shown promise for improving cardiometabolic health [44] but would score poorly on the REAP‐S. Another strength is that the metabolic tests for TC and LDL‐C can be obtained from either a POC device or clinical laboratory, which enhances the future scalability of the LWA beyond clinical settings and provides the additional benefit of obtaining results immediately from POC devices. Finally, the LWA includes subjective and objective measures that include additional and infrequently prioritized patient‐centered outcomes such as a happiness and life satisfaction.

A possible limitation of this research is the limited sample size. However, there is lack of consensus on the minimum sample size required for an EFA. Some guidelines suggest at least 200 cases. Other guidelines suggest taking a ratio of sample size to number of variable approaches. Cattell proposed three to six cases per variable [45]. Gorsuch proposed a five to one ratio [26], and Everitt [46] and Nunnally [47] suggested at least 10 to one. As stated previously, the unweighted least squares extraction method has been shown to recover factors better in small sample sizes when the number of factors is expected to be small (as low as 50) and when communalities are high (> 0.6) [27]. Another potential limitation is that LDL‐C values provided by the POC device were calculated using the Friedewald estimation method, which requires fasting. However, this method has good correlation with directly measured LDL‐C when TG levels are < 400 mg/dL, which was the case for all samples in the present study [48, 49]. If necessary, an adaptable LDL‐C estimation method could be used which provides comparable LDL‐C values between fasting and non‐fasting groups [50]. Finally, our sample consisted of mostly middle‐aged and older adults (mean age 57.3 ± 11.1) so these results may not be generalized to younger populations. Furthermore, the inclusion of people with overweight and obesity may have resulted in a homogeneous sample. However, the parent studies included participants with and without cardiometabolic disease, which would introduce some heterogeneity to the characteristic of interest (i.e., obesity) for better factor recovery [51, 52]. Future research will include a CFA in a new sample and implementation of the final version of the assessment in a primary care clinic as part of an obesity treatment regimen. After an initial CFA is conducted, factor loadings can be combined to develop a scoring procedure for gauging an individual's health status within each dimension [53]. This scoring system could be used as a tool to (1) help guide clinical decision making, (2) promote behavior change as patients work on developing their wellness and (3) monitor patients over time and track their progress throughout the course of an intervention. Additional EFAs and CFAs should be conducted in more diverse age groups, individuals with obesity not seeking treatment, and those disproportionately affected by obesity. The feasibility of implementing the LWA in clinical and under‐resourced settings should also be empirically tested. Such differences may yield varying final factor solutions for the development of other LWA versions.

5. Conclusion

The LWA is a novel and holistic wellness assessment comprised of five wellness dimensions including subjective and objective measures to provide greater context of patient disease burden [54]. Ultimately, this can be used to help guide obesity treatment decisions and take patient‐centered care approaches. The study findings demonstrate that a five‐factor solution is the most parsimonious model with evidence of satisfactory internal consistency and reliability. Measures included in the LWA are resource‐efficient and exhibit low‐patient burden, which maximizes scalability. Future research should include a CFA of the current model and additional CFAs in diverse populations. For example, the LWA could be a useful tool for people undergoing treatment for chronic disease with or without the presence of obesity. It is also applicable to healthy individuals who would like to improve their wellness, such as athletes. Importantly, CFAs should be performed (or EFAs of the original 21 item model when necessary) across diverse age, racial/ethnic, and socioeconomic groups. Finally, there are numerous other applications for the LWA, and implementation strategies should be tested for integration and routine use in primary care practices, personal care, or as a wellness assessment in the workplace or other community settings.

Author Contributions

Data collection, K.M.E.; data analysis, K.M.E. and K.A.S.; data interpretation, K.M.E., K.A.S., R.D.S.; literature search, K.M.E.; generation of figures, K.M.E.; writing—original draft preparation, K.M.E.; writing—review and editing, K.A.S., J.R.F., E.P.P., T.K.C., J.O.H., H.R.W., R.D.S. All authors have read and approved the final manuscript.

Conflicts of Interest

K.M.E., K.A.S., J.R.F., E.P.P., T.K.C., and R.D.S. declares no conflicts of interest. J.O.H. is a member of the General Mills Health and Wellness Advisory Committee, for which he receives an honorarium. H.R.W. and J.O.H. are co‐owners of the State of Slim weight management program, which was the intervention in the affiliated study NCT04014296.

Supporting information

Supporting Information S1