**See Commentary**, pg [1174](https://doi.org/10.1002/oby.22865).

Study ImportanceWhat is already known?►Body weight measurements are routinely collected from patients during clinical encounters and regularly documented in electronic health records systems; however, the construction and use of weight measures for program evaluation and research vary substantially.►Although previous systematic reviews have examined associations between weight‐management programs and patient outcomes, we did not identify any prior review that summarized approaches used by researchers to operationalize and analyze measures of body weight from administrative data.What does this review add?►Among 39 studies reviewed, we found high variability in the reported construction and use of body weight measures, with many studies lacking key information needed to compare findings or replicate analyses.How might these results change the direction of research?►The availability of consistent, replicable methods of weight measurement is critical for longitudinal monitoring of patient health.►Improving methodologies for constructing weight measures will support more robust evidence building, transparency in reporting, and replicable science.

Introduction {#oby22790-sec-0009}
============

Background {#oby22790-sec-0010}
----------

Body weight measurements are routinely collected from patients during clinical encounters and documented in electronic health record (EHR) systems, and they are readily accessible by health care providers to manage patients' health. Collectively, body weight measures documented in EHRs can be utilized for several critical functions, including monitoring patient health outcomes, evaluating health‐related programs and interventions, assessing quality of care, and research. The construction and utilization of body weight measures from EHRs vary substantially in reported literature.

The Veterans Health Administration (VHA) is the largest integrated health system in the United States and is one of the earliest developers and users of a unified EHR starting in the early 1990s ([1](#oby22790-bib-0001){ref-type="ref"}). Data from the EHR are extracted nightly and uploaded into the Department of Veterans Affairs Corporate Data Warehouse (CDW) ([2](#oby22790-bib-0002){ref-type="ref"}). The availability of these relational data tables provides administrators, managers, and researchers opportunities to conduct evaluations of population health, programs, or treatments.

VHA developed the MOVE! Weight Management Program for Veterans (MOVE!), a robust program to address the growing prevalence of obesity among veterans. A recent systematic review examined studies that reported on the quality and effectiveness of MOVE!, often using weight‐related outcome measures in analyses ([3](#oby22790-bib-0003){ref-type="ref"}). However, little attention has been paid to how weight measures are determined using EHR data. The absence of such guidelines limits the ability to compare findings across studies and over time. Given the growing emphasis on the use of pragmatic trials ([4](#oby22790-bib-0004){ref-type="ref"}) that rely on administrative data and the need to generate reproducible and transparent analytic methods ([5](#oby22790-bib-0005){ref-type="ref"}), researchers are increasingly challenged to make their work accessible and replicable. To better understand the diversity of weight measurement methods, we undertook a review of weight measurement processes documented in published literature.

Objective {#oby22790-sec-0011}
---------

The objective of this systematic review was to describe the range of body weight measurement algorithms documented in studies using VHA data. Findings from this review will advance the ability to evaluate and test weight algorithms. This work was guided by VHA policy pertaining to nonresearch activities ([6](#oby22790-bib-0006){ref-type="ref"}). Institutional review board approval was not required because we relied on publicly available materials.

Methods {#oby22790-sec-0012}
=======

Research questions {#oby22790-sec-0013}
------------------

We conducted a systematic literature review to identify the various methodologies researchers utilize to construct body weight measures in VHA studies. Our review followed recommendations in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement ([7](#oby22790-bib-0007){ref-type="ref"}). We were guided by the following two questions: (1) how do researchers define, operationalize, and analyze body weight measures in VHA studies, and (2) what is the range of variation in defined body weight measures across studies?

Literature search strategy and sources {#oby22790-sec-0014}
--------------------------------------

An exploratory literature search was conducted in February 2018, and a large collection of relevant articles was referred to the team by VHA colleagues. These two sources (exploratory search and referrals) served as a preliminary review to focus our objectives, define inclusion and exclusion criteria for the studies, and develop tools to abstract key information from articles. A structured literature search was subsequently conducted in October 2018 using the research database of the National Center for Biotechnology Information/PubMed.

Our focus was on use of body weight measures derived from VHA data systems; thus, we limited our review to studies that used data generated by VHA in the course of caring for veteran patients. Our search strategy specified the following terms: ("veterans health"\[MeSH term\] OR "veterans"\[MeSH term\] OR "hospitals, veterans"\[MeSH term\]) AND ("body weight" OR "body mass index" OR "obesity"). The search excluded animal research, conference proceedings, and systematic reviews.

Eligibility criteria {#oby22790-sec-0015}
--------------------

We selected articles published in peer‐reviewed scholarly journals between 2008 and 2018. Articles were considered eligible for inclusion if the study reported use of body weight measures as an outcome (dependent variable) in analyses and if weight measures relied on existing electronic data originally collected for nonresearch purposes.

An article was excluded when (1) the authors did not report measurement and/or use of a body weight measure, (2) it was not original research (e.g., opinion piece, study protocol), (3) it did not focus on veteran populations, (4) weight measures were self‐reported, (5) VHA administrative data were not used, or (6) weight was not an outcome in analyses.

Data collection {#oby22790-sec-0016}
---------------

We extracted study design details, participant inclusion and exclusion criteria, sample size, years of data extraction, aim or purpose of the weight measure(s), and data source of the relevant weight measure from the identified studies. When study design was not explicitly reported by the authors, determination was guided by the literature ([8](#oby22790-bib-0008){ref-type="ref"}). We also documented whether a weight measure was used as a criterion for sample selection (e.g., study included only individuals with overweight).

Weight measure construction, analysis, and utilization were recorded as well as any described algorithms. Key elements included (1) total time in the study follow‐up time period, (2) number of unique time points considered for analyses, (3) time intervals (the length of time between each data measurement time point; e.g., baseline to 6 months of follow‐up), (4) time windows (the length of time surrounding a time point in which observations are included; e.g., a 1‐month window before/after baseline measurement), (5) missing data (number or percentage), (6) treatment of missing data, (7) definition of outliers or biologically implausible values, (8) treatment of outliers, (9) units of measurement, (10) weight measure calculation (continuous, dichotomous, or categorical variable), and (11) validity assessment of weight data.

Analyses and synthesis of results {#oby22790-sec-0017}
---------------------------------

Two team members (MF and AA) completed an initial exploratory review and summarized findings. All coauthors provided input on development of the analytic plan for the full review. Eligible abstracts were screened (AA and MF). An in‐depth full‐text review of articles was conducted for those meeting initial inclusion criteria (AA and MF).

To ensure validity and inter‐rater reliability of the review process, two rounds of verification based on a 10% random sample of studies meeting inclusion criteria and a 10% sample of those excluded were reviewed by at least one other team member (LD, JB, RE, WW). The reviewers extracted key data elements, which were compared with the initial abstraction. The larger team then discussed extracted data, clarified key elements, and further refined the scope of review. Decisions pertaining to sampling criteria, as well as collection and analysis of key data from the studies, were based on team consensus.

Upon completing the review of all studies, a third verification round (using the same process from the first two rounds) was conducted, yielding 92% inter‐rater agreement on included/excluded studies. Final decisions were made based on team consensus. Abstracted data from included articles were aggregated by content‐specific categories across studies for each of the key elements described. Results were synthesized and summarized into a matrix. Quality of the studies was assessed using selected items from the COnsensus‐based Standards for the selection of health status Measurement INstruments (COSMIN) ([9](#oby22790-bib-0009){ref-type="ref"}).

Results {#oby22790-sec-0018}
=======

Literature review sample {#oby22790-sec-0019}
------------------------

We identified 492 nonduplicated records (Figure [1](#oby22790-fig-0001){ref-type="fig"}) and eliminated 346 articles via abstract screening. Full‐text review was completed for 146 articles plus 6 additional studies identified from citations and not previously included. After full‐text review, we excluded 107 articles, the majority of which were eliminated because of primary data collection for weight measures (*n* = 37) and/or weight was not an outcome (*n* = 44).

![Flow diagram of literature review. \*Articles can meet multiple exclusion criteria; thus, categories will not sum to total. \[Color figure can be viewed at [wileyonlinelibrary.com](https://wileyonlinelibrary.com)\]](OBY-28-1205-g001){#oby22790-fig-0001}

The final sample included 39 studies (Table [1](#oby22790-tbl-0001){ref-type="table"}). The majority (87%) described an observational, retrospective cohort study design (Tables [1](#oby22790-tbl-0001){ref-type="table"}, [2](#oby22790-tbl-0002){ref-type="table"}). Most studies assessed weight outcomes to evaluate weight management programs (41%) ([10](#oby22790-bib-0010){ref-type="ref"}, [11](#oby22790-bib-0011){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [13](#oby22790-bib-0013){ref-type="ref"}, [14](#oby22790-bib-0014){ref-type="ref"}, [15](#oby22790-bib-0015){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [17](#oby22790-bib-0017){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [19](#oby22790-bib-0019){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [25](#oby22790-bib-0025){ref-type="ref"}); the remaining studies evaluated postbariatric surgery weight changes ([25](#oby22790-bib-0025){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [28](#oby22790-bib-0028){ref-type="ref"}, [29](#oby22790-bib-0029){ref-type="ref"}, [30](#oby22790-bib-0030){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [32](#oby22790-bib-0032){ref-type="ref"}, [33](#oby22790-bib-0033){ref-type="ref"}, [34](#oby22790-bib-0034){ref-type="ref"}, [35](#oby22790-bib-0035){ref-type="ref"}). Sample sizes ranged from 40 ([29](#oby22790-bib-0029){ref-type="ref"}) to 4,990,424 ([36](#oby22790-bib-0036){ref-type="ref"}), with most studies (62%) including more than 1,000 patients.

###### 

Characteristics of studies included in systematic review (*n* = 39)

  Author                       Study design                            Study years   Sample size                                                        Population                                                                                                                Aim/purpose
  ---------------------------- --------------------------------------- ------------- ------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  **Adams (2012)**             Retrospective cohort                    2003‐2008     61                                                                 Veterans with obesity who received bariatric surgery, age \< 65 years                                                     Describe prevalence of tobacco and substance use and associated factors that predict weight loss among bariatric patients
  **Arterburn (2013)**         Retrospective cohort                    2000‐2006     516                                                                Veterans with obesity who received bariatric surgery, age not specified                                                   Identify determinants of weight loss post bariatric surgery
  **Baker (2015)**             Retrospective cohort                    2003‐?        1,474                                                              Veterans with rheumatoid arthritis, age \> 18 years                                                                       Identify factors associated with long‐term changes in BMI
  **Batch (2018)**             Retrospective cohort                    2008‐2013     62,882                                                             MOVE! participants, age not specified                                                                                     Determine patterns of change in weight among MOVE! participants
  **Bounthavong (2014)**       Retrospective cohort                    2006‐2010     51,977                                                             Veterans with type 2 diabetes, age ≥ 18 years                                                                             Evaluate differences in BMI among veterans with type 2 diabetes mellitus
  **Braun (2016)**             Retrospective cohort                    2007‐2008     1,659                                                              MOVE! participants, age \< 70 years                                                                                       Evaluate degree of weight loss among MOVE! participants
  **Breland (2017)**           Cross‐sectional                         1999‐2016     4,990,424                                                          Veterans with a primary care visit, age not specified                                                                     Describe obesity prevalence among subpopulations of VHA patients
  **Buta (2018)**              Retrospective cohort                    2001‐2010     248,089                                                            OEF/OIF/OND veterans, age not specified                                                                                   Examine whether posttraumatic stress disorder is associated with change in BMI
  **Chan (2017)**              Longitudinal                            2004‐2014     237,577                                                            MOVE! participants, age not specified                                                                                     Examine the relationship between MOVE! participation and weight outcomes
  **Copeland (2012)**          Retrospective cohort                    2001‐2006     254,051                                                            Veterans with obesity, age not specified                                                                                  Assess receipt of obesity care by patients with and without mental illness
  **Garvin (2015)**            Retrospective cohort                    2008‐2010     216                                                                MOVE! participants, age \< 90 years                                                                                       Examine the effect of high‐intensity MOVE! on achieving at least 5% weight reduction.
  **Garvin & Hardy (2016)**    Retrospective cohort                    2008‐2010     375                                                                MOVE! participants, age not specified                                                                                     Determine whether early weight reduction in MOVE! predicted later participation or achievement of weight reduction goals
  **Garvin & Marion (2015)**   Retrospective cohort                    2008‐2010     404                                                                MOVE! participants, age \< 90 years                                                                                       Explore background and program characteristics associated with a 5% weight reduction in MOVE!
  **Goodrich (2016)**          Retrospective cohort                    2007‐2013     11,188                                                             MOVE! participants with serious mental illness diagnosis, age 18‐69 years                                                 Investigate whether there were sex differences in weight loss and program participation in MOVE!
  **Grabarczyk (2017)**        Retrospective cohort                    2012‐2016     66,035                                                             Veterans with overweight or obesity, age not specified                                                                    Compare the effectiveness of weight management medications
  **Hauser (2010)**            Retrospective cohort                    2003‐2006     70                                                                 Veterans with obesity who received bariatric surgery, age 29‐66 years (range, not inclusion criteria)                     Evaluate the long‐term outcomes following gastric bypass
  **Hoerster (2014)**          Retrospective cohort                    2007‐2012     20,819                                                             MOVE! participants, age not specified                                                                                     Compare the effectiveness of MOVE! among veterans with posttraumatic stress disorder, other mental health conditions, and no mental health diagnoses
  **Huerta (2008)**            Retrospective cohort                    2004‐2006     40                                                                 Veterans with obesity who received bariatric surgery, age not specified                                                   Assess whether preoperative weight loss resulted in favorable outcomes after gastric bypass
  **Huizinga (2010)**          Retrospective cohort                    2001‐2007     18,205                                                             Veterans who had received an oral antidiabetic drug, age ≥ 18 years                                                       Compare the effect of oral diabetes therapy regimens on BMI
  **Ikossi (2010)**            Retrospective cohort                    2001‐2007     24 cohort and 78 control                                           Veterans with obesity who received bariatric surgery, with and without posttraumatic stress disorder, age not specified   Evaluate the effect of posttraumatic stress disorder on excess body weight loss after gastric bypass
  **Jackson (2015)**           Retrospective cohort                    2005‐2012     238,540 MOVE! participants and 1,606,257 matched nonparticipants   MOVE! participants and nonparticipants, age \< 70 years                                                                   Investigate whether MOVE! participation is associated with reduced incidence of diabetes
  **Janney (2016)**            Retrospective cohort                    2008‐2012     84,770                                                             MOVE! participants, age 18‐69 years                                                                                       Investigate the influence of sleep disordered breathing on weight loss
  **Kahwati (2011)**           Case control                            2008‐2010     31,904 MOVE! participants and 71,967 nonparticipants               MOVE! participants and nonparticipants, age not specified                                                                 Explore variation in MOVE! program implementation to identify facility structure, policies, and processes associated with larger patient weight loss outcomes
  **Kazerooni (2016)**         Retrospective cohort                    2000‐2014     767                                                                Veterans who were new topiramate users, age not specified                                                                 Determine whether topiramate use results in significant weight loss.
  **Littman (2012)**           Cross‐sectional and longitudinal        2005‐2008     3,192 MOVE! participants and 73,407 nonparticipants                MOVE! participants and nonparticipants, age 18‐69 years                                                                   Assess the reach and effectiveness of MOVE!
  **Littman (2015)**           Retrospective cohort                    2008‐2012     207,131                                                            Random sample of veterans with a MOVE! visit, age \< 70 years                                                             Determine whether obesity screening and MOVE! participation and outcomes are equitable for patients with serious mental illness and depressive disorder compared with those without
  **Maciejewski (2016)**       Retrospective cohort                    1999‐2004     1,787 cohort and 5,305 controls                                    Veterans with obesity who received bariatric surgery and nonsurgical matched controls, age not specified                  Examine weight change in a large, multisite, clinical cohort of veterans who received gastric bypass compared with nonsurgical matched patients
  **Maguen (2013)**            Retrospective cohort and longitudinal   2001‐2011     496,722                                                            Veterans with a clinical visit after the end of their last deployment, age not specified                                  Explore the relationship between BMI and posttraumatic stress disorder and evaluate trajectories of BMI change
  **Noel (2012)**              Retrospective cohort and longitudinal   2002‐2006     223,246                                                            Veterans with obesity, age ≥ 18 years                                                                                     Determine whether obesity‐related education or a weight management program was associated with declines in BMI
  **Pandey (2018)**            Retrospective cohort                    2004‐2014     213,985                                                            Women veterans, age ≥ 18 years                                                                                            Analyze the association of military sexual trauma and posttraumatic stress disorder with obesity
  **Romanova (2013)**          Retrospective cohort                    2006‐2009     382                                                                MOVE! participants, age not specified                                                                                     Evaluate the effectiveness of MOVE! in achieving weight loss
  **Rosenberger (2011)**       Retrospective cohort                    2001‐2009     16,656                                                             Veterans with sufficient BMI values, age not specified                                                                    Determine BMI trajectories and sociodemographic factors associated with BMI trajectory
  **Rutledge (2011)**          Retrospective cohort                    1998‐2007     60                                                                 Veterans with obesity who received bariatric surgery, age ≤ 65 years                                                      Assess relationship between patient\'s number of psychiatric conditions and weight loss surgery outcomes
  **Rutledge (2012)**          Longitudinal                            1999‐2007     55                                                                 Veterans with obesity who received bariatric surgery, age ≤ 65 years                                                      Examine longitudinal psychiatric treatment changes and changes in weight and weight‐related comorbidities after bariatric surgery
  **Shi (2011)**               Retrospective cohort                    2006‐2008     13,293                                                             Veterans with type 2 diabetes mellitus, age not specified                                                                 Investigate the impact of publicized safety warnings for thiazolidinediones on glycemic outcomes in patients with diabetes mellitus
  **Skancke (2018)**           Retrospective cohort                    2010‐2017     100                                                                Veterans with obesity who received bariatric surgery, age not specified                                                   Benchmark the outcomes of gastrectomy based on recent guidelines
  **Win (2014)**               Retrospective cohort                    2004‐2010     79                                                                 Veterans with obesity who received bariatric surgery, age not specified                                                   Explore the relationship between the number of nutrition visits and change in BMI after gastric bypass surgery
  **Xiao (2017)**              Retrospective cohort                    1998‐2014     1,077                                                              Veterans with a new diagnosis of lymphoma, age not specified                                                              Describe long‐term and short‐term weight change trends, identify factors associated with weight gain, and determine whether weight change during chemotherapy affects time to next treatment and survival
  **Yan (2008)**               Retrospective cohort                    1997‐2002     59                                                                 Veterans with obesity who received bariatric surgery, age not specified                                                   Determine success of gastric bypass as measured by changes in excess weight loss

MOVE!, Weight Management Program for Veterans; OEF, Operation Enduring Freedom; OIF, Operation Iraqi Freedom; OND, Operation New Dawn.

John Wiley & Sons, Ltd

###### 

Characteristics of studies included in systematic review (*n* = 39)

                                                                             *n*   \%
  -------------------------------------------------------------------------- ----- -------
  **Study design** [^a^](#oby22790-note-0002){ref-type="fn"}                        
  **Retrospective cohort**                                                   34    87.2%
  **Longitudinal**                                                           5     12.8%
  **Cross‐sectional**                                                        2     5.1%
  **Case control**                                                           1     2.6%
  **MOVE! / TeleMOVE! Study**                                                       
  **Yes**                                                                    16    41.0%
  **Sample size**                                                                   
  **\< 100 patients**                                                        7     17.9%
  **100‐999 patients**                                                       8     20.5%
  **1,000‐49,999**                                                           9     23.1%
  **50,000‐99,999**                                                          5     12.8%
  ** ≥100,000**                                                              10    25.6%
  **Description of data source** [^a^](#oby22790-note-0002){ref-type="fn"}          
  **National/regional: CDW, NPCD, VISN, VHA administrative data sets**       19    48.7%
  **Local: facility data, EHR, VISTA, CPRS**                                 17    43.6%
  **Program‐related or not specific: MOVE! data, VHA data**                  6     15.4%
  **Total time in follow‐up time period**                                           
  **\< 12 months**                                                           5     12.8%
  **1 year**                                                                 11    28.2%
  **2‐4 years**                                                              12    30.8%
  **≥ 5 years**                                                              7     17.9%
  **Not reported**                                                           1     2.6%
  **Number of time points in data collection**                                      
  **1‐2**                                                                    14    35.9%
  **3‐4**                                                                    10    25.6%
  **5‐6**                                                                    4     10.3%
  **7‐9**                                                                    3     7.7%
  **≥ 10**                                                                   4     10.3%
  **All available (multiple) weight values within time period**              6     15.4%
  **Not reported**                                                           1     2.6%
  **Time intervals reported** [^b^](#oby22790-note-0003){ref-type="fn"}      35    89.7%
  **Time windows reported** [^c^](#oby22790-note-0004){ref-type="fn"}        18    46.2%

Sum more than total because of studies reporting multiple categories.

Time intervals are amount of time between each data collection time point, for example, baseline, 6 months after, and 12 months after baseline.

Time windows correspond to amount of time surrounding data collection time point in which observations are included. For example, 6‐month time point may include observations within 1 month prior or 1 month after 6‐month time point.

CDW, Corporate Data Warehouse; CPRS, Computerized Patient Record System; EHR, electronic health record; MOVE!, Weight Management Program for Veterans, NPCD, National Patient Care Database; TeleMOVE!, Home Telehealth Weight Management Program for Veterans; VHA, Veterans Health Administration; VISN, Veterans Integrated Services Networks; VISTA, Veterans Health Information Systems and Technology Architecture.

John Wiley & Sons, Ltd

Data sources {#oby22790-sec-0020}
------------

Several VHA data sources for weight measures were listed by researchers. Most stated they used national or regional data sources, including the VHA CDW, National Patient Care Database, or other unspecified VHA administrative data sets (49%) ([10](#oby22790-bib-0010){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [17](#oby22790-bib-0017){ref-type="ref"}, [19](#oby22790-bib-0019){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [37](#oby22790-bib-0037){ref-type="ref"}, [38](#oby22790-bib-0038){ref-type="ref"}, [39](#oby22790-bib-0039){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [42](#oby22790-bib-0042){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [44](#oby22790-bib-0044){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}). Two additional studies (5%) in this group reported using a Veterans Integrated Services Network database with unclear linkage to the national CDW ([22](#oby22790-bib-0022){ref-type="ref"}, [46](#oby22790-bib-0046){ref-type="ref"}). Other studies (44%) described using a facility EHR or related systems (Veterans Health Information Systems and Technology Architecture or Computerized Patient Record System) ([11](#oby22790-bib-0011){ref-type="ref"}, [13](#oby22790-bib-0013){ref-type="ref"}, [14](#oby22790-bib-0014){ref-type="ref"}, [15](#oby22790-bib-0015){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [25](#oby22790-bib-0025){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [28](#oby22790-bib-0028){ref-type="ref"}, [29](#oby22790-bib-0029){ref-type="ref"}, [30](#oby22790-bib-0030){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [32](#oby22790-bib-0032){ref-type="ref"}, [33](#oby22790-bib-0033){ref-type="ref"}, [34](#oby22790-bib-0034){ref-type="ref"}, [35](#oby22790-bib-0035){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}, [48](#oby22790-bib-0048){ref-type="ref"}). The remaining studies were not specific about source or how data were extracted, listing MOVE! program data (15%) (10,12,14,16,18,23) or VHA data (8%) (10,14,16).

Time factors {#oby22790-sec-0021}
------------

The time frames from which the studies obtained weight measures spanned from 1997 to 2017 (Table [1](#oby22790-tbl-0001){ref-type="table"}). With the exception of one cross‐sectional study ([36](#oby22790-bib-0036){ref-type="ref"}), all studies established a baseline time period to be compared with a follow‐up period. The baseline weight measure was typically assigned based on the date of an index event, such as a first MOVE! visit, date of bariatric surgery, or clinic visit. The majority (90%) of studies reported the total time in the follow‐up period (90%), most spanning a period of 1 year (28%) ([11](#oby22790-bib-0011){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [30](#oby22790-bib-0030){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}) or 2 to 4 years (31%) ([14](#oby22790-bib-0014){ref-type="ref"}, [15](#oby22790-bib-0015){ref-type="ref"}, [19](#oby22790-bib-0019){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [29](#oby22790-bib-0029){ref-type="ref"}, [33](#oby22790-bib-0033){ref-type="ref"}, [34](#oby22790-bib-0034){ref-type="ref"}, [38](#oby22790-bib-0038){ref-type="ref"}, [42](#oby22790-bib-0042){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}) (Table [2](#oby22790-tbl-0002){ref-type="table"}). Similarly, almost all studies (90%) reported the number of time points from which data were collected. Most often, studies focused on one or two time points (36%) ([10](#oby22790-bib-0010){ref-type="ref"}, [11](#oby22790-bib-0011){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [13](#oby22790-bib-0013){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [29](#oby22790-bib-0029){ref-type="ref"}, [30](#oby22790-bib-0030){ref-type="ref"}, [34](#oby22790-bib-0034){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [38](#oby22790-bib-0038){ref-type="ref"}, [39](#oby22790-bib-0039){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}, [46](#oby22790-bib-0046){ref-type="ref"}). The duration of time between the assessed time points (time intervals) varied across studies but most often included intervals of 6 months (e.g., 6, 12, or 18 months after baseline).

Just under half (46%) of studies described the time windows used to determine point‐in‐time weights, with wide variability in the width of the window as follows: 2 weeks ([37](#oby22790-bib-0037){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}), 30 days or 1 month ([12](#oby22790-bib-0012){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}), 2 months ([12](#oby22790-bib-0012){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}), 3 months ([17](#oby22790-bib-0017){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}), 5 to 7 months ([10](#oby22790-bib-0010){ref-type="ref"}, [19](#oby22790-bib-0019){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}), 10 to 15 months ([11](#oby22790-bib-0011){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [33](#oby22790-bib-0033){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}), or longer ([31](#oby22790-bib-0031){ref-type="ref"}). Several studies reported using more than one time window, often using different windows to define baseline weights versus follow‐up weights ([12](#oby22790-bib-0012){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}). Typically, when multiple weights were found within the defined window, the weight that was recorded closest to the relevant time point was retained for use in analyses. Six studies did not use windows but instead used all available weight values to assess weight trajectories ([11](#oby22790-bib-0011){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [42](#oby22790-bib-0042){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}), one of which calculated a quarterly median value of weights over time ([43](#oby22790-bib-0043){ref-type="ref"}).

Measure construction {#oby22790-sec-0022}
--------------------

There were wide differences in how researchers defined and constructed outcomes (Table [3](#oby22790-tbl-0003){ref-type="table"}). Studies used BMI (49%) or weight in pounds (41%) or kilograms (13%). Parameterization of weight outcomes included assessing change in weight from baseline to a follow‐up time point (62%) ([11](#oby22790-bib-0011){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [17](#oby22790-bib-0017){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [24](#oby22790-bib-0024){ref-type="ref"}, [25](#oby22790-bib-0025){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [28](#oby22790-bib-0028){ref-type="ref"}, [29](#oby22790-bib-0029){ref-type="ref"}, [30](#oby22790-bib-0030){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [32](#oby22790-bib-0032){ref-type="ref"}, [33](#oby22790-bib-0033){ref-type="ref"}, [34](#oby22790-bib-0034){ref-type="ref"}, [35](#oby22790-bib-0035){ref-type="ref"}, [37](#oby22790-bib-0037){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}, [46](#oby22790-bib-0046){ref-type="ref"}) or weight trajectory over time (23%) ([11](#oby22790-bib-0011){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [38](#oby22790-bib-0038){ref-type="ref"}, [42](#oby22790-bib-0042){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}, [48](#oby22790-bib-0048){ref-type="ref"}) as continuous variables. Several studies (46%) created a binary indicator to classify participants meeting a specific weight threshold (e.g., at least 5% weight loss) and then calculated the proportion of participants meeting this threshold ([10](#oby22790-bib-0010){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [13](#oby22790-bib-0013){ref-type="ref"}, [14](#oby22790-bib-0014){ref-type="ref"}, [15](#oby22790-bib-0015){ref-type="ref"}, [17](#oby22790-bib-0017){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [39](#oby22790-bib-0039){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [44](#oby22790-bib-0044){ref-type="ref"}, [45](#oby22790-bib-0045){ref-type="ref"}). Eleven (28%) studies used more than one approach. For example, Maciejewski et al. ([31](#oby22790-bib-0031){ref-type="ref"}) determined the percentage change in weight at follow‐up as compared with baseline, predicted the percentage of weight change at several follow‐up time periods, and assessed whether patients lost a specified percentage of their baseline weight.

###### 

Construction of weight measures among studies included in systematic review (*n* = 39)

  Characteristic[^a^](#oby22790-note-0006){ref-type="fn"}             *n*   \%
  ------------------------------------------------------------------- ----- -------
  **Unit of measurement**                                                    
  **Weight in pounds**                                                16    41.0%
  **Weight in kilograms**                                             5     12.8%
  **BMI**                                                             19    48.7%
  **Use of weight variable in analyses**                                     
  **Change in weight compared with baseline**                         24    61.5%
  **Weight trajectory over time**                                     9     23.1%
  **Proportion of participants meeting weight loss/gain threshold**   18    46.2%
  **Missing weight measures**                                                
  **Number or percentage of missing values reported**                 14    35.9%
  **Not reported**                                                    23    59.0%
  **Not applicable (e.g., because of sampling)**                      2     5.1%
  **Treatment of missing weight measures**                                   
  **Exclusion of individual**                                         15    38.5%
  **Exclusion of observation**                                        4     10.3%
  **Not reported**                                                    22    56.4%
  **Outlier definition**                                                     
  **Absolute values**                                                 12    30.8%
  **Relative, calculated, and/or data derived values**                8     20.5%
  **Not reported**                                                    25    64.1%
  **Treatment of outliers**                                                  
  **Exclusion of individual**                                         2     5.1%
  **Exclusion of observation**                                        12    30.8%
  **Not reported**                                                    25    64.1%
  **Validation strategies reported**                                  6     15.4%

Studies may report more than one category; thus, categories are not mutually exclusive.

John Wiley & Sons, Ltd

Missing values {#oby22790-sec-0023}
--------------

Only 14 (36%) studies reported the number or percentage of missing weight values ([12](#oby22790-bib-0012){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [37](#oby22790-bib-0037){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}) (Table [3](#oby22790-tbl-0003){ref-type="table"}). Missing values were handled by excluding the patient from the analyses (39%) ([12](#oby22790-bib-0012){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [19](#oby22790-bib-0019){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [27](#oby22790-bib-0027){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [37](#oby22790-bib-0037){ref-type="ref"}, [41](#oby22790-bib-0041){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [44](#oby22790-bib-0044){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}, [48](#oby22790-bib-0048){ref-type="ref"}) or excluding only the relevant missing weight value (10%) ([20](#oby22790-bib-0020){ref-type="ref"}, [26](#oby22790-bib-0026){ref-type="ref"}, [37](#oby22790-bib-0037){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}). Two studies in particular described using a combination of both methods (5%) ([37](#oby22790-bib-0037){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}). Notably, among studies that reported exclusion at the level of the patient, several assessed their cohorts longitudinally over multiple years with repeated measures. Thus, it is likely that the exclusion occurred when all the weight values were absent for an individual participant rather than excluding a participant based on any single value that was missing for a particular visit. The reported methods often did not allow us to distinguish between exclusions made because all values were missing versus exclusions because one or more values were missing. Finally, two studies described using all available weight values over a specified time period ([44](#oby22790-bib-0044){ref-type="ref"}, [48](#oby22790-bib-0048){ref-type="ref"}).

Outlier values {#oby22790-sec-0024}
--------------

Most studies (64%) did not identify outlier values (Table [3](#oby22790-tbl-0003){ref-type="table"}). Of those that did, some (31%) used specific cutoff values based on lower and upper thresholds ([12](#oby22790-bib-0012){ref-type="ref"}, [16](#oby22790-bib-0016){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [23](#oby22790-bib-0023){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}, [40](#oby22790-bib-0040){ref-type="ref"}, [42](#oby22790-bib-0042){ref-type="ref"}, [43](#oby22790-bib-0043){ref-type="ref"}, [47](#oby22790-bib-0047){ref-type="ref"}). For example, Maguen et al. ([42](#oby22790-bib-0042){ref-type="ref"}) and Noel et al. ([43](#oby22790-bib-0043){ref-type="ref"}) identified 70 lb and 700 lb as lower and upper thresholds, respectively. Fewer studies (21%) used a more complex process for determining outliers based on statistical properties of the analytic sample ([10](#oby22790-bib-0010){ref-type="ref"}, [12](#oby22790-bib-0012){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [31](#oby22790-bib-0031){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}). For example, Chan et al. ([12](#oby22790-bib-0012){ref-type="ref"}) computed mean weight by individual and defined outliers as individual values more than 3 SDs from an individual's mean. Similarly, Maciejewski et al. ([31](#oby22790-bib-0031){ref-type="ref"}) calculated rolling SD of consecutive weight values in groups of three and then used these as thresholds for determining outliers. Breland et al. ([36](#oby22790-bib-0036){ref-type="ref"}) determined outlier values by defining specific upper‐ and lower‐bound threshold values of the sequential ratios of current weight to previous weight and current weight to next weight. Six studies used both simple thresholds and more complex statistical approaches ([12](#oby22790-bib-0012){ref-type="ref"}, [18](#oby22790-bib-0018){ref-type="ref"}, [20](#oby22790-bib-0020){ref-type="ref"}, [21](#oby22790-bib-0021){ref-type="ref"}, [22](#oby22790-bib-0022){ref-type="ref"}, [36](#oby22790-bib-0036){ref-type="ref"}). Outliers were addressed by excluding the participant from the analyses (5%) or excluding only the outlier value from analyses (31%).

Validity {#oby22790-sec-0025}
--------

Validation of weight measures was rarely reported (15% of studies). One study (27) conducted a sensitivity analysis that varied the threshold value for weight change over time, while another ([43](#oby22790-bib-0043){ref-type="ref"}) performed sensitivity analysis of a 5% weight change threshold. Additional validation strategies included alternative analyses by adjusting statistical models ([20](#oby22790-bib-0020){ref-type="ref"}), comparing results to other similar studies ([16](#oby22790-bib-0016){ref-type="ref"}), comparing participants with one weight measure to those with multiple weight measures ([42](#oby22790-bib-0042){ref-type="ref"}), and examining within‐subject changes in the trajectory of the weight measure ([47](#oby22790-bib-0047){ref-type="ref"}).

Study quality {#oby22790-sec-0026}
-------------

Study quality and risk of bias was evaluated in terms of the construction of the weight outcome measure and the reported number of relevant attributes (e.g., time factors, outlier values). Most studies (56%) described in some level of detail at least half of the selected attributes. However, only 10 (26%) studies met an excellent level of quality for all relevant measure attributes. Many (46%) had a substantial lack of reported attributes for the weight measure, which hinders an adequate assessment of the validity of findings.

Discussion {#oby22790-sec-0027}
==========

We found high variability in the reported construction and use of body weight measures among VHA studies, with many lacking key information needed to compare findings or replicate analyses. Although most studies (90%) reported the amount of time in the follow‐up period and the number of time points assessed, just over half reported time windows (54%) or identified outlier values (51%), and fewer reported missing weight values (34%) or validation strategies (15%). Interestingly, 11 of the 39 reviewed studies constructed more than one weight measure as outcome variables in analyses, while 8 studies identified more than one time window, and 6 studies used multiple definitions for outlier values. Thus, the variation in measurement specification was not only present across studies but within studies as well. Often, studies lacked a rationale or justification for including multiple sets of defined measurement criteria.

Though our search was not limited to studies of weight change for veterans with overweight or obesity, most (41%) were evaluations of MOVE!, 28% evaluated veterans after bariatric surgery, and 10% focused on receipt of obesity‐related care within VHA. Thus, the implications of our findings are especially relevant for assessments of patient weight in the context of weight loss interventions. Moreover, EHR and associated data warehouses within integrated health systems are generating larger and more comprehensive data sources that include many health‐related measures beyond weight (e.g., blood pressure, cholesterol). The issues highlighted in this systematic review are likely to be prevalent for these additional measures as well. Importantly, the availability of consistent, replicable methods of measurement over time are particularly valuable in longitudinal monitoring of health status, an approach frequently used in population‐based studies.

Despite the frequent use of weight measures in research studies and program evaluations, our findings highlight the absence of standardized guidelines for defining, constructing, utilizing, and reporting weight measurement approaches, pointing to the need for more consistent methodologies. This is a key issue, especially in light of increasing use of administrative data for program evaluation and the rise of pragmatic clinical trials ([4](#oby22790-bib-0004){ref-type="ref"}) that often rely on these data sources. In order to foster the use of valid and reliable measurement approaches among clinical trials and evaluations, the data infrastructure to support such research should be strengthened by standardized data extraction and analytic processes ([49](#oby22790-bib-0049){ref-type="ref"}).

The development of standardized measurement approaches for weight data should consider employing suggested methods for handling missing data ([50](#oby22790-bib-0050){ref-type="ref"}, [51](#oby22790-bib-0051){ref-type="ref"}) and outliers ([52](#oby22790-bib-0052){ref-type="ref"}, [53](#oby22790-bib-0053){ref-type="ref"}). In a previous review of reported treatments for missing data from 169 articles published in the journal *Prevention Science*, researchers found that deletion‐based techniques (i.e., listwise \[exclusion of individuals\] and pairwise \[exclusion of observations\] deletion) were the most commonly reported methods for handling missing data, used by 30% of the studies ([54](#oby22790-bib-0054){ref-type="ref"}). Moreover, based on how the studies reported missing data, the authors concluded that this proportion was likely higher and closer to 37% ([54](#oby22790-bib-0054){ref-type="ref"}), similar to our finding of 44% of studies. Deletion‐based techniques, however, are considered to be poor solutions for addressing missing data ([55](#oby22790-bib-0055){ref-type="ref"}), as there are more sophisticated methods available.

Multiple imputation in particular is becoming increasingly used and recommended as a way to decrease bias because of missing data ([54](#oby22790-bib-0054){ref-type="ref"}, [56](#oby22790-bib-0056){ref-type="ref"}, [57](#oby22790-bib-0057){ref-type="ref"}). The review mentioned above identified the use of imputation strategies by 21% of studies ([54](#oby22790-bib-0054){ref-type="ref"}). In contrast, none of the 39 studies included in this review used imputation methods, although in many cases, we were unable to distinguish whether missing data techniques were not employed or not reported. Certainly, there are circumstances whereby other methods of handling missing data may be more appropriate than imputation ([58](#oby22790-bib-0058){ref-type="ref"}). However, the lack of detail reported by studies that pertain to the treatment of missing data is a common problem, even among those using imputation methods ([50](#oby22790-bib-0050){ref-type="ref"}, [51](#oby22790-bib-0051){ref-type="ref"}, [56](#oby22790-bib-0056){ref-type="ref"}). Regardless of the method selected, studies would benefit from additional reporting clarity and transparency about how missing data were addressed.

Parallel to the issue of missing data, the majority (64%) of studies we reviewed did not state how outlier weight values were defined or handled. This is consistent with a previous review that found that 41% of 42 large epidemiologic studies did not address biologically implausible values, while an additional 26% reported insufficient information regarding implausible values ([59](#oby22790-bib-0059){ref-type="ref"}). Similar to our approach, the authors categorized biologically implausible values into the following three groups: externally defined limits (i.e., comparisons to values obtained outside of the study, or "absolute" values), internally defined limits (i.e., defined based on the values of the study sample, or "relative" values), and using a combination of both methods ([59](#oby22790-bib-0059){ref-type="ref"}).

Among the studies we reviewed that did report outliers, there was wide variability in outlier definitions in both the "absolute" and "relative" groups. For example, multiple cutoff values were used by our sample to define plausible weight values, ranging from 50 to 91 lb for a minimum weight and from 600 to 823 lb for a maximum weight value. Several variations of BMI cutoff values were also reported. Similarly, the studies that used relative approaches also differed in their strategies to identify outlier values, such as using calculated participant mean weights, SDs, weight ratios, and/or weight change. Interestingly, six of the studies used a combination of approaches. Numerous statistical approaches to address outlier or implausible values have been described elsewhere, along with recommendations for selecting and utilizing an appropriate method ([52](#oby22790-bib-0052){ref-type="ref"}, [53](#oby22790-bib-0053){ref-type="ref"}). Yet, the finding that only 14 (36%) distinct studies in our sample described any information related to outliers indicates, again, that there are opportunities to improve reporting of basic elements of data analyses.

Lastly, our sample of studies was decidedly split between constructing the weight outcome as a continuous versus categorical or binary variable. In fact, a substantial number of our reviewed studies (46%) created a binary indicator to designate whether or not a study participant met a weight threshold. As evident from the variability in the construction of weight outcomes, there does not appear to be a consistent practice for defining weight measures or defining a clinically meaningful change in weight. Although several studies considered meaningful weight change as a loss in excess weight of 5% or more, rationale for this threshold was not reported. Lack of consensus for operationalizing weight outcomes limits the ability to compare and contrast findings across studies. Moreover, categorizing or dichotomizing continuous variables can lead to a loss of information and biased results ([60](#oby22790-bib-0060){ref-type="ref"}). Further exploration of weight measures will require careful consideration of what constitutes clinically meaningful weight change and how measurement approaches can be constructed to optimally detect these changes while limiting the risk of bias.

Robust science requires transparency and replicability, which allow others to validate and potentially extend findings. Researchers are increasingly requested by funding agencies and peer‐reviewed journals to share data and provide clear descriptions of analytic methods. Several standards for reporting research studies have been published by expert working groups to guide such documentation ([61](#oby22790-bib-0061){ref-type="ref"}, [62](#oby22790-bib-0062){ref-type="ref"}, [63](#oby22790-bib-0063){ref-type="ref"}, [64](#oby22790-bib-0064){ref-type="ref"}). These guidelines should extend to the need for clear articulation of methods used to construct and analyze health system--generated data.

 {#oby22790-sec-0028}

This study had some limitations. Our review of weight algorithms was limited to VHA administrative data sources and studies of Veteran populations. VHA has long emphasized the importance of assessing and documenting weight for screening, preventing, and treating obesity ([21](#oby22790-bib-0021){ref-type="ref"}, [65](#oby22790-bib-0065){ref-type="ref"}). Additionally, large relational databases of clinical and administrative data fed by one of the earliest integrated EHR systems in the world provide a robust data infrastructure to support quality improvement, program evaluation, and research studies. Thus, our findings may not be generalizable to studies outside VHA using non‐VHA data sources. We did not verify the data sources reported by researchers and noted variation in the terminology they used to describe these sources. Thus, it is possible some reported sources were misclassified. Our specification of search terms and/or exclusion criteria may have led to missed opportunities to further understand approaches to derive weight‐related measures. Lastly, the lack of reporting on a key data element in an article does not necessarily indicate absence but rather may simply reflect space constraints for published research and lack of priority for detailed documentation.

Conclusion {#oby22790-sec-0030}
----------

Routinely collected clinical measures of patient health, including body weight, captured by administrative systems are important sources for outcome measures in research and program evaluation. The high variability found in measurement approaches, along with the expanding availability of large data systems, challenges researchers to identify reliable, valid, and consistent methodologies for constructing measures based on administrative data. Improving these methods will support more robust evidence building, transparency in reporting, and replicable science. Future research should examine whether specific analytic approaches in constructing weight measures can potentially add value to the validity or robustness of the analyses and/or negatively impact data collection or other logistical aspects of the study.

Funding agencies {#oby22790-sec-0031}
================

This work was supported by the National Center for Health Promotion and Disease Prevention, VHA. The views expressed in this article are those of the authors and do not necessarily reflect the policy or position of the Department of Veterans Affairs or the US Government.

Disclosure {#oby22790-sec-0032}
==========

The authors declared no conflict of interest.

We would like to thank our colleagues Jane Kim, Michael Goldstein, Sophia Califano, Margaret Dundon, Sophia Hurley, Eugene Oddone, and Felicia McCant for ongoing support for this work, including reviewing drafts of this manuscript and providing valuable feedback.
