CoChat
Sign Up
Cited by 52Mobile Health and mHealth ApplicationsObesity, Physical Activity, DietHealth Literacy and Information Accessibility

Utilization patterns and user characteristics of an ad libitum Internet weight loss program

Open access
Binks, Martin, van Mierlo, Trevor·2010·Journal of Medical Internet Research
DOI 10.2196/jmir.1347PMC PMC2872772
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: The Internet holds promise for the delivery of evidence-based weight loss treatment to underserved populations. However, most studies do not reflect the more naturalistic and common ad libitum, or freely at will, use of the Internet. Randomized clinical trials, for example, typically include at least some direct contact with participants and often have restrictive selection criteria. There is a paucity of research examining utilization patterns of online weight loss programs, particularly in the rapidly expanding direct-to-consumer arena. OBJECTIVES: To examine self-reported characteristics (age, body mass index [BMI], gender), behaviors, and Internet site utilization patterns of a sample of users of a direct-to-consumer ad libitum Internet weight loss program. METHODS: This study is based on analysis of archival data from the initial 15 weeks of an ongoing, free, evidence-based, direct-to-consumer Internet weight loss program, the Healthy Weight Center, which included standard information about nutrition, fitness, and behavioral strategies; monitoring tools; and moderated support group message boards. Participants encountered the program through self-directed Internet searches and anonymously registered to utilize the site. Self-reported user characteristics and electronically tracked utilization data were extracted from existing program data, compiled, and examined. Pearson correlations were computed to examine the association of program utilization with age and BMI. One-way analysis of variance (ANOVA) was used for gender comparisons. RESULTS: We examined data from the first 204 adult users of the program who were classified as either overweight (BMI 25 to < 30 kg/m(2)) or obese (BMI > or = 30 kg/m(2)). The mean age of participants was 42.0 years (SD 11.7), 81.9% (167/204) were women, and mean BMI was 32.01 kg/m(2) (SD 6.26). The percent of participants who used program tools was as follows: 13.7%, meal planner; 10.8%, nutrition lookup: 17.6%, activity log; 14.2%, journal; and 22.1%, weight tracker. Participants also used the following educational resources: nutrition, 13.2%; fitness, 6.4%; and behavioral, 7.4%. Of the personal self-assessments available through the program, 57.8% of participants assessed personal barriers, and 50.5% assessed relationship with food. Only 7.8% used the support group message boards. No significant associations between site utilization and age, gender, or BMI were found. Reasons for wanting to lose weight were: health, 87%; appearance, 74%; mobility, 44%; doctor recommendation, 23%; and spouse/friend suggested, 12%. The age participants reported first becoming overweight was young adulthood, 31%; late adulthood, 28%; childhood, 22%; adolescence, 17%; and as a toddler, 3%. Self-perceived factors contributing to weight gain were lack of exercise for 70% of participants, emotions for 62%, overeating for 61%, and slow metabolism for 33%. CONCLUSIONS: Internet weight loss programs reach many people who cannot access traditional treatment. However, users appear not to be optimally utilizing key aspects of the weight loss intervention, such as education, monitoring, and support. This study provides insight into the patterns of ad libitum use of an online weight loss program across multiple treatment-related domains in a naturalistic Internet environment.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/jmir.1347

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The 90-9-1 rule classifies user participation into three categories: Lurkers, Contributors, and Superusers, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute significantly to the content.

    “The 90–9–1 rule classifies user participation into three categories; specifically, Lurkers, Contributors, and Superusers, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute significantly to the content (15, 17, 18, 22, 23).”

    From Trouble with the curve: the 90-9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training (2024) · cites this paper as [15]

    The Results section on Utilization Patterns reports how often participants accessed tools in an Internet weight-loss program, not a 90–9–1 classification of participation. Searches for “90-9-1,” “lurkers,” “contributors,” “superusers,” and the stated percentages found no passage in our copy explaining that rule. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

  • Current Web-based interventions often reach higher-educated women.

    “However, several researchers have shown that current Web-based interventions often reach higher-educated women, as seen in several studies [13-17]”

    From How to increase reach and adherence of web-based interventions: a design research viewpoint (2015) · cites this paper as [14]

  • Most Web-based interventions reach a limited group only, while aiming for a broad audience.

    “On top of this, most interventions reach a limited group only, while aiming for a broad audience.”

    From How to increase reach and adherence of web-based interventions: a design research viewpoint (2015) · cites this paper as [14]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [11]van Mierlo T Cunningham J Anonymous Internet-based interventions for problem drinkers: design, development and dissemination 2010-02-01 35th Annual Alcohol Epidemiology Symposium of the Kettil Bruun Society June 5, 2009 Copenhagen, Denmark http://www.kbs2009.dk/abstracts-kbs2009.pdf

Cited by

Sorted by citations

Related

Authors
Binks, M., & van Mierlo, T.
Corresponding author
Binks, Martin
ORCID
Binks, Martin
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2010-03-29
Volume
12
Issue
1
Pages
e9
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/jmir.1347
PMC
PMC2872772
OpenAlex
W2039485132
Cited by
52
References
14
Citations by year
2025: 1 · 2024: 1 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 7 · 2015: 4 · 2014: 2 · 2013: 8 · 2012: 12
FWCI
15.92
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
Keywords
user characteristics, overweight and obesity, barriers to weight loss, relationship with food, underserved populations, age differences
MeSH terms
Adult, Aged, Female, Humans, Health Knowledge, Attitudes, Practice, Male, Middle Aged, Motivation, Obesity, Patient Compliance, Patient Education as Topic, Self Care, Social Support, United States, Program Evaluation, Weight Loss, Body Mass Index, Self Efficacy, Internet, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review