The DOI matches its Crossref record; 35 of the 37 references it cites were found in a registry or an index; none of the 13 sources its claims cite is checked for support yet.
Using mHealth Technology to Enhance Self-Monitoring for Weight Loss
Open accessAbstract
Background Self-monitoring for weight loss has traditionally been performed with paper diaries. Technologic advances could reduce the burden of self-monitoring and provide feedback to enhance adherence. Purpose To determine if self-monitoring diet using a PDA only or the PDA with daily tailored feedback (PDA+FB), was superior to using a paper diary on weight loss and maintenance. Design The Self-Monitoring and Recording Using Technology (SMART) Trial was a 24-month RCCT; participants were randomly assigned to one of three self-monitoring groups. Setting/participants From 2006 to 2008, 210 overweight/obese adults (84.8% female, 78.1% white) were recruited from the community. Data were analyzed in 2011. Intervention Participants received standard behavioral treatment for weight loss which included dietary and physical activity goals, encouraged the use of self-monitoring, and was delivered in group sessions. Main outcome measures Percentage weight change at 24 months, adherence to self-monitoring over time. Results Study retention was 85.6%. The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss. For adherence to self-monitoring, there was a time-by-treatment group interaction between the combined PDA groups and the paper diary group (p=0.03) but no difference between PDA and PDA+FB groups (p=0.49). Across all groups, weight loss was greater for those who were adherent ≥60% versus <30% of the time, p<0.001. Conclusions PDA+FB use resulted in a small weight loss at 24 months; PDA use resulted in greater adherence to dietary self-monitoring over time. However, for sustained weight loss, adherence to self-monitoring is more important than the method used to self-monitor. A daily feedback message delivered remotely enhanced adherence and improved weight loss, which suggests that technology can play a role in improving weight loss.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In a 24-month randomized trial of 210 overweight/obese adults, PDA-based self-monitoring with daily tailored feedback produced a small significant 24-month weight loss, while PDA use without feedback improved adherence to dietary self-monitoring more than paper diaries. Across groups, greater self-monitoring adherence was associated with greater weight loss, suggesting adherence matters more than self-monitoring method.
Key findings
- Mean percentage weight loss at 24 months did not differ among groups: paper diary −1.94% (95% CI −3.88, 0.01), PDA −1.38% (95% CI −3.38, 0.62), PDA+FB −2.32% (95% CI −4.29, −0.35); only PDA+FB showed significant within-group loss (p=0.02).
- Adherence to self-monitoring showed a time-by-treatment interaction between the combined PDA groups and the paper diary group (p=0.03), with no PDA vs PDA+FB difference (p=0.49); more PDA participants were adherent ≥60% than paper diary participants (PDA vs paper p=0.03; PDA+FB vs paper p=0.01).
- Across all groups, weight loss was greater for those adherent ≥60% versus <30% (p<0.001); at 18 months, 19%–20% of PDA groups remained adherent ≥30% compared with 8% of the paper diary group.
- Study retention was 85.6% in the abstract and 85.7% at 24 months in the results.
Methods
- 24-month RCT; 210 overweight/obese adults recruited from the community in 2006–2008 (84.8% female, 78.1% white); randomized to paper diary, PDA, or PDA+FB; all received standard behavioral weight loss treatment in group sessions.
- Primary outcome: percentage weight change at 24 months; secondary outcome: adherence to dietary self-monitoring, defined as percentage of days recording adequate calories, with nonadherence assumed if a diary was not submitted.
- Analysis: intent-to-treat; linear mixed modeling with linear contrasts, ANOVA, and generalized estimating equations; missing weight observations handled by assuming a 0.3 kg/month increase; SAS 9.1; two-sided significance at 0.05.
Limitations
- Stated: male representation was disappointing; recruitment strategies targeting men were implemented, but many eligible men did not complete screening. The paper also states that the impact of more-proximal feedback needs further investigation and that the novel design makes comparisons challenging.
- Stated/design: adherence to self-monitoring was limited to 18 months because there was only one contact in the final 6 months.
- Evident: the sample was predominantly female (84.8%) and white (78.1%), limiting generalizability; adherence relied on self-monitoring records, and missing weight data were imputed rather than fully observed.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
18 Own result · 13 Own method · 13 Own interpretation · 8 Not checked yet
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A significant within-group difference in weight over time was found for the PDA+FB group (p=0.02) with an average 2.32% weight loss but not for the other two groups (paper diary: – 1.94%, PDA: –1.38%).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results > Percentage Weight Change
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across all groups, weight loss was greater for those who were adherent ≥60% versus <30% of the time, p<0.001.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For adherence to self-monitoring, there was a time-by-treatment group interaction between the combined PDA groups and the paper diary group (p=0.03) but no difference between PDA and PDA+FB groups (p=0.49).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For adherence to self-monitoring, there was a time-by-treatment group interaction between the combined PDA groups and the paper diary group (p=0.03) but no difference between PDA and PDA+FB groups (p=0.49).”
- Also stated in: § Self-Monitoring · plain
“Difference in adherence over time between the combined PDA and PDA+FB group and the paper diary group was significant (p=0.03).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Self-Monitoring · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At 6 months, a higher proportion of the PDA+FB group achieved ≥5% weight loss; however, this was not sustained at 24 months.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results > Percentage Weight Change
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite the weight regain among some participants, mean weight at 24 months was below the mean baseline weight.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results > Percentage Weight Change
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 13
- The quote only partly states this claim. 1
- Probably not a substantial claim. 8
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, 52% (n=110) lost ≥5% of baseline body weight at 6 months; of that group, 19% retained that weight loss at 24 months with no differences among the groups.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results > Percentage Weight Change
plainn = 110- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A daily feedback message delivered remotely enhanced adherence and improved weight loss, which suggests that technology can play a role in improving weight loss.”
- Also stated in: § Conclusion · boosted
“In addition, the current results demonstrate that a daily feedback message delivered remotely enhanced adherence and improved weight loss, which has major implications for the potential use of technology such as smartphones in improving weight loss treatment.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Conclusions
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Conclusion · boosted
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Study retention was 85.6%.”
- Also stated in: § Results · plain
“Retention at 24 months was 85.7% with no differential attrition (see Figure 1).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- Also stated in: § Results · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, at 18 months, the weight losses in the PDA groups were similar between those who were in the upper two categories of adherence (>60% and 30%–59%), suggesting that those who were using a PDA were able to maintain the weight loss at a lower level of self-monitoring adherence after engaging in this behavior for several months.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Since then, the evidence has continued to build, demonstrating a consistent relationship between self-monitoring and weight loss7 and weight loss maintenance.8”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Background
What the sources say
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The sample size of 210 was chosen to have at least 0.80 power for two-sided hypothesis testing via linear contrasts from a linear mixed model at a significance level of 0.05 when comparing changes in weight at 6, 12, 18 and 24 months relative to baseline between (1) the combined PDA groups (PDA, PDA+FB) and the paper diary group at a standardized mean difference (d) of 0.48 and (2) the PDA group”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Methods > Statistical Analysis
plainn = 210- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For each cohort, participants were randomly assigned, with equal allocation via a computer-implemented minimization algorithm that stratified on gender and race, to either the Paper Diary, PDA, or PDA+FB group.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Methods > Design Overview
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“At each assessment, the participants’ weight was measured on a digital scale by study staff.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Methods > Main Outcome Measures
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“A wealth of empirical evidence demonstrates that immediate positive reinforcement for a desired behavior leads to increases in the behavior; the more proximal the reinforcer to the desired behavior, the more likely it will be to increase the desired behavior.13”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Background
What the sources say
- Not checked yetcites [13] Powers, 1976
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Completers also had a lower baseline BMI (33.74 (4.44) vs 35.62 (4.47), p=0.04).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Two studies that focused on self-monitoring methods reported an association between self-monitoring and weight loss; however, neither reported group differences in weight loss.25, 26”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Discussion
What the sources say
- Not checked yetcites [25] Helsel, 2007, Comparison of techniques for self-monitoring eating and exercise behaviors on weight loss in a correspondence-based intervention, J Am Diet Assoc source
- Not checked yetcites [26] Yon, 2007, Personal digital assistants are comparable to traditional diaries for dietary self-monitoring during a weight loss program, J Behav Med source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Completers were older than noncompleters (47.66 (8.38) years vs 41.77(10.95) years, p<0.001), which may have been influenced by three pregnancy-related withdrawals.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“After participants were trained in their assigned self-monitoring method, session materials were identical for the groups.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Methods > Intervention
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The rapid advancement and adoption of wireless devices provide numerous opportunities for the incorporation of mHealth interventions for the delivery of feedback to individuals engaged in weight loss treatment.14, 15”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Background
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Paper diaries are time-consuming and tedious to complete.
- Rests on an assumption: Paper diaries limit the timeliness of supportive and motivational feedback delivered by the interventionist due to the lag time between receipt of the paper diary and delivery of feedback to the participant.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“when subgroups by level of adherence were examined, method of self-monitoring was notably related to self-monitoring adherence, and that, in turn, was related to weight loss maintenance (Table 1).”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results of this 2-year trial underscore the fact that adherence to self-monitoring is more important than the method chosen to self-monitor; however, the use of technology-based tools markedly improved self-monitoring adherence in a larger number and those participants lost more weight and also sustained the loss.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Conclusion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The treatment groups did not differ by sociodemographic or baseline anthropometric measures.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Intent-to-treat principle was used, where subjects were analyzed as randomized.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Methods > Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, more participants in the PDA groups achieved ≥60% adherence; those individuals lost more weight and maintained most of it.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Two studies have shown enhanced adherence with PDA use; however, the observation period was limited to 1 month.27, 28”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Discussion
What the sources say
- Not checked yetcites [27] Beasley, 2008, Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial, J Am Coll Nutr source
- Not checked yetcites [28] Glanz, 2006, Improving dietary self-monitoring and adherence with hand-held computers: a pilot study, Am J Health Promot source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Paper diaries are time-consuming and tedious to complete.
“The most frequently used method to self-monitor diet is the paper diary, which can be timeconsuming and tedious to complete.”
The paper relies on this without support.
Paper diaries limit the timeliness of supportive and motivational feedback delivered by the interventionist due to the lag time between receipt of the paper diary and delivery of feedback to the participant.
“Paper diaries also limit the timeliness of supportive and motivational feedback delivered by the interventionist due to the lag time between receipt of the paper diary and delivery of feedback to the participant.”
The paper relies on this without support.
No other study has examined the effect of mobile technology with or without feedback on weight loss and adherence to self-monitoring during a 2-year behavioral treatment study.
“No other study has examined the effect mobile technology with or without feedback on weight loss and adherence to self-monitoring during a 2-year behavioral treatment study.”
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?
In the citing paper
“Greater improvement in health behaviors is produced by intensive behavioral treatments, that is, those that incorporate multiple counseling sessions, each lasting ≥10 minutes.18-20,22–24,29–31”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [23]
In the citing paper
“Findings confirm modest but significant impacts of brief physician advice or intervention on smoking cessation, improved dietary quality, and increased physical activity.14,15,19,22–27”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [23]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Conclusions
“A daily feedback message delivered remotely enhanced adherence and improved weight loss”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
“no difference between PDA and PDA+FB groups (p=0.49).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Abstract > Results
“Study retention was 85.6%.”
§ Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial > Results
“Retention at 24 months was 85.7% with no differential attrition (see Figure 1).”
Citations
Cites
Comparison of Weight-Loss Diets with Different Compositions of Fat, Protein, and Carbohydrates
Sacks, F. M., Bray, G. A., Carey, V. J., Smith, S. R., Ryan, D. H., Anton, S. D., McManus, K., Champagne, C. M., Bishop, L. M., Laranjo, N., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0804748 · Cited by 1,966 · cited 1×
“29 Foster and colleagues compared a low-carbohydrate to a low-fat diet and conducted group sessions.”
BackgroundLong-term weight loss maintenance
Wing, R. R., & Phelan, S. (2005) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/82.1.222s · Cited by 1,941 · cited 1×
“8 Few studies have tested strategies for long-term maintenance of weight loss 9–11 ; however, research provides evidence that ongoing contact is a strategy that enhances long-term adherence.”
BackgroundSelf-monitoring in weight loss: a systematic review of the literature
Burke, L. E., Wang, J., & Sevick, M. A. (2011) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2010.10.008 · Cited by 1,203 · cited 1×
“6 Since then, the evidence has continued to build, demonstrating a consistent relationship between self-monitoring and weight loss 7 and weight loss maintenance.”
BackgroundSelf-citationIntroduction to sample size determination and power analysis for clinical trials
Lachin, J. M. (1981) · Controlled Clinical Trials · doi:10.1016/0197-2456(81)90001-5 · Cited by 1,012
Artinian, N. T., Fletcher, G. F., Mozaffarian, D., Kris-Etherton, P., Van Horn, L., Lichtenstein, A. H., Kumanyika, S., Kraus, W. E., Fleg, J. L., Redeker, N. S., et al. (2010) · Circulation · doi:10.1161/cir.0b013e3181e8edf1 · Cited by 919 · cited 1×
“12 The use of an electronic diary with automatic feedback could potentially serve as a form of contact.”
BackgroundSelf-citationA self-regulation program for maintenance of weight loss
Wing, R. R., Tate, D. F., Gorin, A. A., Raynor, H. A., & Fava, J. L. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa061883 · Cited by 830 · cited 2×
“10, 11 Although improved self-monitoring adherence did not result in better weight loss maintenance, when subgroups by level of adherence were examined, method of self-monitoring was notably related to self-monitoring adherence, and that, in turn, was related to weight loss maintenance (Table 1).”
BackgroundRandomized Trial of Lifestyle Modification and Pharmacotherapy for Obesity
Wadden, T. A., Berkowitz, R. I., Womble, L. G., Sarwer, D. B., Phelan, S., Cato, R. K., Hesson, L. A., Osei, S. Y., Kaplan, R., & Stunkard, A. J. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa050156 · Cited by 794 · cited 1×
“23 Adherence in the first 6 months of the POUNDS LOST trial predicted weight reduction throughout the trial.”
BackgroundSvetkey, L. P. (2008) · JAMA · doi:10.1001/jama.299.10.1139 · Cited by 748 · cited 1×
“10, 11 Although improved self-monitoring adherence did not result in better weight loss maintenance, when subgroups by level of adherence were examined, method of self-monitoring was notably related to self-monitoring adherence, and that, in turn, was related to weight loss maintenance (Table 1).”
BackgroundA Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial
Patrick, K., Raab, F., Adams, M. A., Dillon, L., Zabinski, M., Rock, C. L., Griswold, W. G., & Norman, G. J. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1100 · Cited by 671
Goodpaster, B. H., DeLany, J. P., Otto, A. D., Kuller, L., Vockley, J., South-Paul, J. E., Thomas, S. B., Brown, J., McTigue, K., Hames, K. C., et al. (2010) · JAMA · doi:10.1001/jama.2010.1505 · Cited by 558 · cited 1×
“3,4 The core behavioral change strategies, based on social cognitive theory, include multiple components.”
Background
Research works printed but not linked: 2
- Burke, 2009, SMART trial: a randomized clinical trial of self-monitoring in behavioral weight management-design and baseline findings, Contemp Clin Trials, 30, 540, 10.1016/j.cct.2009.07.003 · DOI · Paper page
- Burke, 2011, The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial, Obesity, 19, 338, 10.1038/oby.2010.208 · DOI · Paper page
Cited by
Sorted by citations
Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Patel, V., & Haines, A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001362 · Cited by 1,766
“Three trials reported no statistically significant effects on weight of a diary-plus-mobile-phone daily feedback messages compared to a diary [54], a text message based intervention [55], and a twitter, podcast, and mobile phone application based intervention [56].”
Allcott, H., & Rogers, T. (2014) · American Economic Review · doi:10.1257/aer.104.10.3003 · Cited by 1,340
“…and Volpp 2013; Royer, Stehr, and Sydnor 2013), smoking (Gine, Karlan, and Zinman 2010; Volpp et al. 2009), weight loss (Anderson et al. 2009; Burke et al. 2012; John et al. 2011), water conservation (Ferraro, Miranda, and Price 2011; Ferraro and Price 2013), academic performance (Jackson…”
Lifestyle modification approaches for the treatment of obesity in adults.
Wadden, T. A., Tronieri, J. S., & Butryn, M. L. (2020) · American Psychologist · doi:10.1037/amp0000517 · Cited by 661
“In RCTs, participants assigned to monitor food intake with smartphone apps completed significantly more food records than those instructed to keep paper diaries (Burke et al., 2012; Spring et al., 2017).”
BackgroundBurke, L. E., Ma, J., Azar, K. M. J., Bennett, G. G., Peterson, E. D., Zheng, Y., Riley, W., Stephens, J., Shah, S. H., Suffoletto, B., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000232 · Cited by 516
Self-citationTurner-McGrievy, G. M., Beets, M. W., Moore, J. B., Kaczynski, A. T., Barr-Anderson, D. J., & Tate, D. F. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001510 · Cited by 373
Wang, Y., Xue, H., Huang, Y., Huang, L., & Zhang, D. (2017) · Advances in Nutrition · doi:10.3945/an.116.014100 · Cited by 310
Crowdsourcing Applications for Public Health
Brabham, D. C., Ribisl, K. M., Kirchner, T., & Bernhardt, J. M. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.10.016 · Cited by 264
Integrating Technology Into Standard Weight Loss Treatment: A Randomized Controlled Trial
Spring, B., Duncan, J. M., Janke, E. A., Kozak, A. T., McFadden, H. G., DeMott, A., Pictor, A., Epstein, L. H., Siddique, J., Pellegrini, C. A., et al. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.1221 · Cited by 234
“In contrast, prior weight loss interventions mediated by mobile devices sent automated rather than personally crafted text messages and delayed feedback on participants’ hand-delivered data for at least a week because coaches needed time to study it.(12,26,28,29) When used to record diet and activity without behaviora…”
Bond, D. S., Thomas, J. G., Raynor, H. A., Moon, J., Sieling, J., Trautvetter, J., Leblond, T. L., & Wing, R. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100821 · Cited by 210
Patel, M. L., Wakayama, L. N., & Bennett, G. G. (2021) · Obesity · doi:10.1002/oby.23088 · Cited by 213
“Secondary aim 2: Comparison of digital versus paperbased selfmonitoring Nine trials (representing 34 comparisons) reported engagement outcomes between participants who selfmonitored via digital health and those who selfmonitored via paper (35,40,44,49,59,66,69,73,80).”
BackgroundPapakonstantinou, E., Oikonomou, C., Nychas, G., & Dimitriadis, G. D. (2022) · Nutrients · doi:10.3390/nu14040823 · Cited by 199
Perspectives on the Evolution of Mobile (mHealth) Technologies and Application to Rehabilitation
Dicianno, B. E., Parmanto, B., Fairman, A. D., Crytzer, T. M., Yu, D. X., Pramana, G., Coughenour, D., & Petrazzi, A. (2014) · Physical Therapy · doi:10.2522/ptj.20130534 · Cited by 197
Better Population Health Through Behavior Change in Adults: A Call to Action
Spring, B., Ockene, J. K., Gidding, S. S., Mozaffarian, D., Moore, S., Rosal, M. C., Brown, M. D., Vafiadis, D. K., Cohen, D. L., Burke, L. E., et al. (2013) · Circulation · doi:10.1161/01.cir.0000435173.25936.e1 · Cited by 195
Self-citationCognitive behavioral therapy to aid weight loss in obese patients: current perspectives
Castelnuovo, G., Pietrabissa, G., Manzoni, G. M., Cattivelli, R., Rossi, A., Novelli, M., Varallo, G., & Molinari, E. (2017) · Psychology Research and Behavior Management · doi:10.2147/prbm.s113278 · Cited by 187
“Moreover, the mHealth applications have achieved positive results in adult obesity 118,122 and in childhood obesity too.”
BackgroundMobile apps for healthy living: Factors influencing continuance intention for health apps
Yan, M., Filieri, R., Raguseo, E., & Gorton, M. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120644 · Cited by 182
Wharton, C. M., Johnston, C. S., Cunningham, B. K., & Sterner, D. A. (2014) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2014.04.291 · Cited by 191
mHealth approaches to child obesity prevention: successes, unique challenges, and next directions
Tate, E. B., Spruijt‐Metz, D., O’Reilly, G. A., Jordan‐Marsh, M., Gotsis, M., Pentz, M. A., & Dunton, G. F. (2013) · Translational Behavioral Medicine · doi:10.1007/s13142-013-0222-3 · Cited by 180
“A second benefit is that mHealth tools have the capacity to collect data frequently, unobtrusively, and in interactive ways that can boost self-monitoring adherence in weight loss programs [50].”
BackgroundUsing a Wearable Camera to Increase the Accuracy of Dietary Analysis
O’Loughlin, G., Cullen, S., McGoldrick, A., O’Connor, S., Blain, R. J., O’Malley, S., & Warrington, G. D. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2012.11.007 · Cited by 169
Form of delivery as a key ‘active ingredient’ in behaviour change interventions
Dombrowski, S. U., O’Carroll, R. E., & Williams, B. (2016) · British Journal of Health Psychology · doi:10.1111/bjhp.12203 · Cited by 177
Awareness and Use of mHealth Apps: A Study from England
Kayyali, R., Peletidi, A., Ismail, M. F., Hashim, Z., Bandeira, P., & Bonnah, J. (2017) · Pharmacy · doi:10.3390/pharmacy5020033 · Cited by 158
“ineff ctive f th participant i unwill g to b cons stent and honest, and it also loses its effectiveness if it i overused [27,28].”
BackgroundLee, H. E., & Cho, J. (2016) · Health Communication · doi:10.1080/10410236.2016.1167998 · Cited by 163
“While a number of functions have been identified (Burke et al., 2012; Helander, Kaipainen, Korhonen, & Wansink, 2014; West et al., 2013; Wharton, Johnston, Cunningham, & Sterner, 2014), the following have been most commonly addressed: (1) tracking diet/fitness activities (e.g., daily history), (2)…”
BackgroundThe relative validity of a food record using the smartphone application MyFitnessPal
Teixeira, V., Voci, S. M., Mendes-Netto, R. S., & da Silva, D. G. (2017) · Nutrition & Dietetics · doi:10.1111/1747-0080.12401 · Cited by 166
Schembre, S. M., Liao, Y., Robertson, M. C., Dunton, G. F., Kerr, J., Haffey, M. E., Burnett, T., Basen-Engquist, K., & Hicklen, R. S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8701 · Cited by 154
“In 10 studies [21,25,30,31, 33,35-37,43,49], feedback was available continuously, in 3 [27,45,46], feedback was provided at multiple times daily, and in 18 [22-24,26,28,29,32,34,38-42,44,47,48,50,51,53], feedback was provided once daily.”
BackgroundMethodInfluentialMobile Health Applications in Weight Management: A Systematic Literature Review
Dounavi, K., & Tsoumani, O. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.12.005 · Cited by 154
“These include the effortless self-monitoring of diet and physical activity, tailored feedback provision, reminders for app use, and interaction with peers.(48,63) The integration of such features increases user engagement with the treatment, which, in turn, promotes successful weight management.”
BackgroundChin, S. O., Keum, C., Woo, J., Park, J., Choi, H. J., Woo, J., & Rhee, S. Y. (2016) · Scientific Reports · doi:10.1038/srep34563 · Cited by 143
Abaza, H., & Marschollek, M. (2017) · Methods of Information in Medicine · doi:10.3414/me17-05-0003 · Cited by 138
“Others, such as mPOD [64, 65] and DietMatePro [66, 67], allowed recording dietary intake and physical activity, and accordingly provided individualized counseling via twitter or phone calls [68].”
MethodSteinberg, D. M., Bennett, G., & Svetkey, L. (2017) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2017.1628 · Cited by 124
Munir, F., Biddle, S., Davies, M. J., Dunstan, D. W., Esliger, D., Gray, L. J., Jackson, B. R., O'Connell, S. E., Yates, T. E., & Edwardson, C. L. (2018) · BMC Public Health · doi:10.1186/s12889-018-5187-1 · Cited by 140
“result in greater achievement of behaviour change goals [28].”
BackgroundLeung, L., & Chen, C. (2019) · Telecommunications Policy · doi:10.1016/j.telpol.2019.01.005 · Cited by 151
Mobile Apps for Weight Management: A Review of the Latest Evidence to Inform Practice
Ghelani, D. P., Moran, L., Johnson, C., Mousa, A., & Naderpoor, N. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00412 · Cited by 134
Lyzwinski, L. N. (2014) · Journal of Personalized Medicine · doi:10.3390/jpm4030311 · Cited by 124
DeVito Dabbs, A., Song, M. K., Myers, B. A., Li, R., Hawkins, R. P., Pilewski, J. M., Bermudez, C. A., Aubrecht, J., Begey, A., Connolly, M., et al. (2016) · American Journal of Transplantation · doi:10.1111/ajt.13701 · Cited by 120
Review of Innovations in Digital Health Technology to Promote Weight Control
Thomas, J. G., & Bond, D. S. (2014) · Current Diabetes Reports · doi:10.1007/s11892-014-0485-1 · Cited by 126
Mason, A. E., Jhaveri, K., Cohn, M., & Brewer, J. A. (2018) · Journal of Behavioral Medicine · doi:10.1007/s10865-017-9884-5 · Cited by 106
“2011), a mixed methods intervention that included in-person group meetings and smartphone-based dietary selfmonitoring (85%; Burke et al., 2012), and a mobile intervention that included text messages, apps, coaching calls, and emails that targeted weight-gain prevention (88%; Allman-Farinelli et al.”
BackgroundTechnology-based interventions in the treatment of overweight and obesity: A systematic review
Raaijmakers, L. C. H., Pouwels, S., Berghuis, K. A., & Nienhuijs, S. W. (2015) · Appetite · doi:10.1016/j.appet.2015.07.008 · Cited by 114
“Most interventions in these structured programs were based on Bandura's Social Cognitive Theory (Allen et al., 2013; Bandura, 1986; Blomfield et al., 2014; Burke et al., 2012; Cadmus-Bertram et al., 2013; Collins et al., 2012; Morgan et al., 2011).”
BackgroundMethodInfluentialSteinberg, D., Levine, E. L., Askew, S., Foley, P. B., & Bennett, G. G. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2844 · Cited by 110
“Indeed, evidence does show that electronic self-monitoring via either Web or mobile devices produces greater adherence over traditional paper-and-pencil methods [4,7,8] and improves attitudes toward self-monitoring behaviors [9].”
InfluentialIT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review
Jiang, J., & Cameron, A. (2020) · MIS Quarterly · doi:10.25300/misq/2020/15108 · Cited by 120
Steinberg, D. M., Bennett, G. G., Askew, S., & Tate, D. F. (2015) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.12.011 · Cited by 104
Þorgeirsson, T., & Kawachi, I. I. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2012.10.008 · Cited by 119
Forman, E. M., Goldstein, S. P., Crochiere, R. J., Butryn, M. L., Juarascio, A. S., Zhang, F., & Foster, G. D. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz137 · Cited by 94
Hutchesson, M., Rollo, M. E., Callister, R., & Collins, C. E. (2014) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.07.036 · Cited by 108
Hutchesson, M., Tan, C. Y., Morgan, P. J., Callister, R., & Collins, C. E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4100 · Cited by 99
“Burke et al [9] randomly assigned participants to self-monitor their dietary intake using 3 approaches: a personal digital assistant with no feedback; a personal digital assistant with daily tailored automated feedback; or a paper-based diary with no feedback.”
The Dozen Things Experimental Economists Should Do (More of)
Czibor, E., Jimenez‐Gomez, D., & List, J. A. (2019) · Southern Economic Journal · doi:10.1002/soej.12392 · Cited by 102
“…2013; Royer et al., 2015; Acland and Levy, 2015), smoking cessation (Volpp et al., 2006, 2009; Giné et al., 2010), weight loss (Volpp et al., 2008; Burke et al., 2012; John et al., 2011; Anderson et al., 2009), charitable giving (Meier, 2007; Shang and Croson, and Frey (2016) offer a review of…”
Thomas, J. G., Bond, D. S., Raynor, H. A., Papandonatos, G. D., & Wing, R. R. (2019) · Obesity · doi:10.1002/oby.22410 · Cited by 108
“(14) showed that, compared with paper diaries, use of electronic tools for tracking diet and providing feedback produced the best adherence to self-monitoring and weight loss outcomes at 24 months, although this study involved in-person treatment.”
BackgroundMaxhuni, A., Muñoz-Meléndez, A., Osmani, V., Perez, H., Mayora, O., & Morales, E. F. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.01.008 · Cited by 93
“[26] suggests thatmobile assessment providesmore efficacious strategy for trackingmood andbehaviour change in mental disorders than the traditional paper diaries.”
BackgroundForman, E. M., Goldstein, S. P., Zhang, F., Evans, B. C., Manasse, S. M., Butryn, M. L., Juarascio, A. S., Abichandani, P., Martin, G. J., & Foster, G. D. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/iby016 · Cited by 77
“One randomized controlled trial evaluated a JITAI based on self-monitoring entries using a personal digital assistant that provided ongoing, adaptive dietary feedback throughout a 2-year behavioral weight loss program [24].”
BackgroundMethodMcCarroll, R., Eyles, H. C., & Mhurchú, C. N. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.08.022 · Cited by 87
Technology- and Phone-Based Weight Loss Intervention
Hartman, S. J., Nelson, S. H., Cadmus-Bertram, L. A., Patterson, R. E., Parker, B. A., & Pierce, J. P. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.024 · Cited by 86
A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial
Pellegrini, C. A., Duncan, J. M., Moller, A. C., Buscemi, J., Sularz, A., DeMott, A., Pictor, A., Pagoto, S., Siddique, J., & Spring, B. J. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-1041 · Cited by 90
“Many different technologies have been used in weight loss interventions including PDAs [15,16], cell phone text messages [17,18] and wearable physical activity monitors [19,20].”
MethodKozak, A. T., Buscemi, J., Hawkins, M. A. W., Wang, M. L., Breland, J. Y., Ross, K. M., & Kommu, A. (2016) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9805-z · Cited by 86
“…of eHealth treatments may increase self-monitoring, which is a key component of traditional weight loss programs (Berkman et al., 2014; Burke et al., 2011, 2012; Stone et al., 2003) In the moment feedback Participants can receive ‘‘just in time’’ encouragement and reinforcement…”
The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring
Burke, L. E., Zheng, Y., Ma, Q., Mancino, J., Loar, I., Music, E., Styn, M. A., Ewing, L. J., French, B. F., Sieworek, D., et al. (2017) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2017.03.017 · Cited by 89
“(Burke et al., 2012b; Burke et al., 2006a) Relying only on online screening and excluding the 5-day run-in period of self-monitoring may have contributed to our uncharacteristically low retention rate of 74%.”
BackgroundMethodInfluentialSelf-citationBehavioral Economics Guidelines with Applications for Health Interventions
Luoto, J., & Carman, K. G. (2014) · doi:10.18235/0009206 · Cited by 87
mHealth taxonomy: a literature survey of mobile health applications
Olla, P., & Shimskey, C. (2015) · Health and Technology · doi:10.1007/s12553-014-0093-8 · Cited by 84
“Examples include the blood glucose meter [2], weight scales [7], pulse oximeters which connects directly to mobile phone.”
BackgroundHales, S. B., Turner‐McGrievy, G. M., Wilcox, S., Fahim, A., Davis, R. E., Huhns, M. N., & Valafar, H. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.07.003 · Cited by 78
Steinberg, D., Levine, E. L., Lane, I. B., Askew, S., Foley, P. B., Puleo, E., & Bennett, G. G. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2996 · Cited by 74
“Despite its effectiveness, adherence to traditional paper-based approaches declines over time [7,9].”
Khokhar, B. A., Jones, J., Ronksley, P. E., Armstrong, M. J., Caird, J. K., & Rabi, D. M. (2014) · BMC Obesity · doi:10.1186/s40608-014-0022-4 · Cited by 69
“98] were deemed appropriate for inclusion for the review [20-25] and the purpose of metaanalysis (Figure 1).”
BackgroundMethodInfluentialObesity and outpatient rehabilitation using mobile technologies: the potential mHealth approach
Castelnuovo, G., Manzoni, G. M., Pietrabissa, G., Corti, S. P., Giusti, E. M., Molinari, E., & Simpson, S. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00559 · Cited by 77
“…2013). mHealth applications have also been implemented with promising applications and results in weight loss protocols and obesity rehabilitation (Chomutare et al., 2011; Burke et al., 2012; Cafazzo et al., 2012; Fiordelli et al., 2013; Martinez-Perez et al., 2013; Turner-McGrievy et al., 2013).”
BackgroundSobnath, D., Philip, N. Y., Kayyali, R., Nabhani‐Gebara, S., Pierscionek, B. K., Vaes, A. W., Spruit, M. A., & Kaimakamis, E. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4951 · Cited by 75
“Self-registration techniques such as diaries or tools to measure variables are other features identified to be useful for patients with COPD [23].”
Oh, B., Cho, B., Han, M. K., Choi, H.-C., Lee, M. N., Kang, H., Lee, C. H., Yun, H., & Kim, Y.-H. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4222 · Cited by 72
“uHealth care, an abbreviation for ubiquitous health care, refers to a health care medical service in which information and communication technologies are combined with medicine so that patients can be provided with prevention, diagnosis, treatment, and follow-up services anytime and anywhere, even if they do not direc…”
A Theory-Based Exercise App to Enhance Exercise Adherence: A Pilot Study
Voth, E. C., Oelke, N. D., & Jung, M. E. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4997 · Cited by 72
“Electronic diaries facilitate (1) the instantaneous transfer of data between user and counselor or health care provider [37] and (2) have been shown to be associated with higher rates of adherence when compared to traditional self-monitoring via paper and pen diaries [38].”
BackgroundThe Use of mHealth to Deliver Tailored Messages Reduces Reported Energy and Fat Intake
Ambeba, E. J., Ye, L., Sereika, S. M., Styn, M. A., Acharya, S. D., Sevick, M. A., Ewing, L. J., Conroy, M. B., Glanz, K., Zheng, Y., et al. (2014) · The Journal of Cardiovascular Nursing · doi:10.1097/jcn.0000000000000120 · Cited by 71
Self-citationAssociations Between Self-Monitoring and Weight Change in Behavioral Weight Loss Interventions
Goldstein, S. P., Goldstein, C. M., Bond, D. S., Raynor, H. A., Wing, R. R., & Thomas, J. G. (2019) · Health Psychology · doi:10.1037/hea0000800 · Cited by 63
“Adherence to self-monitoring of dietary intake was assessed as the number of days per week that participants recorded at least 50% of the daily calorie goal or recorded at least 3 separate eating occasions (Burke et al., 2012; Thomas & Wing, 2013).”
MethodResultOrmel, H. L., van der Schoot, G. G. F., Westerink, N.-D. L., Sluiter, W. J., Gietema, J. A., & Walenkamp, A. (2018) · Supportive Care in Cancer · doi:10.1007/s00520-018-4263-5 · Cited by 67
Smartphone applications to support weight loss: current perspectives
Pellegrini, C. A., Pfammatter, A. F., Conroy, D. E., & Spring, B. J. (2015) · Advanced Health Care Technologies · doi:10.2147/ahct.s57844 · Cited by 67
Yingling, L., Mitchell, V. M., Ayers, C. R., Peters-Lawrence, M. H., Wallen, G. R., Brooks, A. T., Troendle, J. F., Adu‐Brimpong, J., Thomas, S., Henry, J. L., et al. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0454-0 · Cited by 61
Consumer perspectives on mHealth for weight loss: a review of qualitative studies
Lyzwinski, L. N., Caffery, L. J., Bambling, M., & Edirippulige, S. (2017) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x17692722 · Cited by 59
“Both of these features are common to electronic interventions including previous mHealth interventions.(9,10) mHealth distinguishes Centre for Online Health and Department of Psychiatry, University of Queensland, School of Medicine, Woolloongabba, QLD, Australia Corresponding author: Lynnette N Lyzwinski, Centre for O…”
Applying Psychological Theories to Promote Long-Term Maintenance of Health Behaviors
Joseph, R. P., Daniel, C. L., Thind, H. K., Benitez, T. J., & Pekmezi, D. (2014) · American Journal of Lifestyle Medicine · doi:10.1177/1559827614554594 · Cited by 68
Sarcona, A., Kovacs, L., Wright, J., & Williams, C. L. (2017) · American Journal of Health Education · doi:10.1080/19325037.2017.1335630 · Cited by 60
“22 The types of mobile-based apps utilized among the college population vary dramatically based on popularity, features, etc.”
BackgroundCastelnuovo, G., Pietrabissa, G., Manzoni, G. M., Corti, S. P., Ceccarini, M., Borrello, M., Giusti, E. M., Novelli, M., Cattivelli, R., Middleton, N. A., et al. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01557 · Cited by 64
“The mHealth approach has shown positive evidence not only in adult obesity (Tufano and Karras, 2005; Burke et al., 2012), but also in pediatric obesity (Jensen et al., 2012; TurnerMcGrievy et al., 2013).”
BackgroundComparison of Older and Younger Adults’ Attitudes Toward the Adoption and Use of Activity Trackers
Kim, S., & Choudhury, A. (2020) · JMIR mHealth and uHealth · doi:10.2196/18312 · Cited by 51
“Wearable devices and other mobile technology for health care allow users to continuously track and manage health data without having to see their health care provider, such as diabetes management [18] and weight loss [19].”
BackgroundKim, M., Kim, Y., Go, Y., Lee, S., Na, M., Lee, Y., Choi, S., & Choi, H. J. (2020) · JMIR mHealth and uHealth · doi:10.2196/14817 · Cited by 56
“Although there are some interventions that use custom algorithms to provide individualized feedback, they only focus on diet, physical activity, weight loss, or any two of these [27,28,32,46].”
InfluentialLin, P., Grambow, S. C., Intille, S. S., Gallis, J. A., Lazenka, T., Bosworth, H. B., Voils, C. I., Bennett, G. G., Batch, B. C., Allen, J., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/10471 · Cited by 53
Citoni, B., Figliuzzi, I., Presta, V., Volpe, M., & Tocci, G. (2021) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-021-00492-4 · Cited by 59
“Indeed, available evidence suggests that incorporating self-management behaviours, such as home BP monitoring, in educational interventions may be an effective approach for ameliorating BP control, as an example by promoting physical activity and weight loss [33].”
BackgroundApplying Behavioral Tools to the Design of Health Projects
Pinto, D. M., Ibarrarán, P., Stampini, M., Carman, K. G., Guanais, F. C., Luoto, J., Sanchez, M. A., & Cali, J. (2014) · doi:10.18235/0008508 · Cited by 53
“The average effect, however, was only around 2% of baseline weight (Burke et al., 2012).”
BackgroundMilitello, L. K., Melnyk, B. M., Hekler, E. B., Small, L., & Jacobson, D. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4398 · Cited by 51
“Feedback messages may play a pivotal role in goal attainment, through behavioral principles such as reinforcement and correction, and serve as a compass enabling individuals to stay on course [49].”
BackgroundAdherent Use of Digital Health Trackers Is Associated with Weight Loss
Pourzanjani, A., Quisel, T., & Foschini, L. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152504 · Cited by 57
Carter, M. C., Burley, V. J., & Cade, J. E. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4520 · Cited by 53
“Burke et al [17] found that moderate-and high-frequency users had lost roughly equivalent amounts of weight at 18 months, whereas the findings from this trial of My Meal Mate suggest that high-frequency users had lost a statistically significantly greater amount of weight than moderate users.”
MethodResultJimoh, F. O., Lund, E. K., Harvey, L. J., Frost, C., Lay, W. J., Roe, M. A., Berry, R., & Finglas, P. M. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7702 · Cited by 51
“This was unexpected and is not consistent with what has been previously reported [33,57,62].”
ResultDietary Self-Monitoring in Weight Management: Current Evidence on Efficacy and Adherence
Yu, Z., Sealey‐Potts, C., & Rodriguez, J. C. (2015) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2015.04.005 · Cited by 52
The Future of Mobile Health Applications and Devices in Cardiovascular Health
Kelli, H. M., Witbrodt, B. C., & Shah, A. J. (2017) · EMJ Innovation · doi:10.33590/emjinnov/10310778 · Cited by 46
Can healthy people benefit from health apps?
Husain, I., & Spence, D. (2015) · BMJ · doi:10.1136/bmj.h1887 · Cited by 45
Raber, M. R., Liao, Y., Rara, A., Schembre, S. M., Krause, K. J., Strong, L., Daniel, C. R., & Basen-Engquist, K. (2021) · Public Health Nutrition · doi:10.1017/s136898002100358x · Cited by 49
Lee, J. E., Lee, D. E., Kim, K., Shim, J. E., Sung, E., Kang, J., & Hwang, J. (2017) · Nutrition Research and Practice · doi:10.4162/nrp.2017.11.3.247 · Cited by 43
“Two mobile-based intervention studies in obese adults have shown that self-monitoring and feedback on physical activity and eating habits via mobile phone significantly decreased Body mass index (BMI) and energy intake in the intervention group [7,9].”
BackgroundInfluentialMacPherson, M., Merry, K. J., Locke, S., & Jung, M. E. (2019) · JMIR mhealth and uhealth · doi:10.2196/12956 · Cited by 46
“In support of this, studies have shown that adherence to daily self-monitoring is associated with increased weight loss [19], and self-monitoring of daily exercise is associated with increased PA and weight loss [18].”
Fazzino, T. L., Sporn, N. J., & Befort, C. A. (2016) · Supportive Care in Cancer · doi:10.1007/s00520-016-3149-7 · Cited by 39
“requirements and provide more real-time feedback [44, 45].”
Arens‐Volland, A., Spassova, L., & Bohn, T. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.08.006 · Cited by 45
Uncovering the invisible patient work system through a case study of breast cancer self-management
Gorman, R. K., Wellbeloved-Stone, C. A., & Valdez, R. S. (2018) · Ergonomics · doi:10.1080/00140139.2018.1503339 · Cited by 42
“This finding aligns with well-documented evidence that mHealth and other forms of CHIT, such as the patient portal, can have positive impacts on adherence work (Burke et al. 2012; Chang et al. 2011; Sullivan et al. 2016; TurnerMcGrievy et al. 2013).”
ResultDias, S. B., Oikonomidis, Y., Diniz, J. A., Baptista, F., Carnide, F., Bensenousi, A., Botana, J. M., Tsatsou, D., Stefanidis, K., Gymnopoulos, L. P., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.898031 · Cited by 42
“In another 24-month RCT, namely, Self-Monitoring and Recording Using Technology (SMART) (120), involving 210 adults with overweight/obesity, it has shown that the use of PDA with appropriate feedback contributed to the adults’ dietary behavior change, resulting in significant weight loss when compared to those using pa…”
MethodSpoelstra, S., Given, C. W., Sikorskii, A. E., Coursaris, C. K., Majumder, A., DeKoekkoek, T., Schueller, M., & Given, B. A. (2015) · Oncology nursing forum · doi:10.1188/15.onf.647-657 · Cited by 41
Rumbo-Rodríguez, L., Sánchez‐SanSegundo, M., Ruiz‐Robledillo, N., Albaladejo-Blázquez, N., Ferrer‐Cascales, R., & Zaragoza-Martí, A. (2020) · Nutrients · doi:10.3390/nu12123634 · Cited by 46
“The provision of feedback could be effective as a complement to interventions carried out using technology to promote weight loss [52,53].”
BackgroundResultInfluentialBehavioral Lifestyle Intervention in the Treatment of Obesity
Looney, S. M., & Raynor, H. A. (2013) · Health Services Insights · doi:10.4137/hsi.s10474 · Cited by 43
Holzmann, S. L., & Holzapfel, C. (2019) · Journal of Personalized Medicine · doi:10.3390/jpm9020031 · Cited by 38
“Adherence and engagement to self-monitoring often appears in the early stage of interventions and declines continuously over time [67,70,71].”
BackgroundAnauati, M. V., Galiani, S., & Weinschelbaum, F. (2015) · Latin American Economic Review · doi:10.1007/s40503-015-0025-7 · Cited by 37
“For instance, a recent randomized two-year trial that tested the role of feedback through SMS messages found that daily messages enhanced adherence to self-monitoring and increased weight loss (Burke et al. 2012).”
BackgroundHu, E. A., Nguyen, V. L., Langheier, J., & Shurney, D. W. (2020) · Journal of Medical Internet Research · doi:10.2196/19634 · Cited by 35
mHealth: Technology for nursing practice, education, and research
Doswell, W. M., Braxter, B., Dabbs, A. D., Nilsen, W., & Klem, M. L. (2013) · Journal of Nursing Education and Practice · doi:10.5430/jnep.v3n10p99 · Cited by 36
“…ecological momentary assessment (EMA) surveys delivered via smartphone, Dr. Burke is conducting a study of real time data collection from a smartphone to retrieve weight, daily diaries, actigraph and accelerometer to study the triggers of relapse during and following intentional weight loss [45] .”
BackgroundDigital Health Solutions for Weight Loss and Obesity: A Narrative Review
Irvin, L., Madden, L. A., Marshall, P., & Vince, R. V. (2023) · Nutrients · doi:10.3390/nu15081858 · Cited by 34
Pfammatter, A. F., Nahum-Shani, I. B., Dezelar, M., Scanlan, L., McFadden, H. G., Siddique, J., Hedeker, D., & Spring, B. J. (2019) · Contemporary Clinical Trials · doi:10.1016/j.cct.2019.05.007 · Cited by 36
Tung, J. Y., Stead, B. E., Mann, W., Pham, B., & Popović, M. R. (2015) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2014.02.0064 · Cited by 34
Remotely engaged: Lessons from remote monitoring in multiple sclerosis
Engelhard, M., Patek, S. D., Sheridan, K., Lach, J., & Goldman, M. D. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.01.006 · Cited by 33
“In a mobile-enabled weight loss intervention, adherence to dietary selfmonitoring declined from roughly 70% to less than 20% over the course of the study[18].”
BackgroundIncorporating Mobile Phone Technologies to Expand Evidence-Based Care
Jones, D. J., Anton, M., Gonzalez, M., Honeycutt, A., Khavjou, O., Forehand, R., & Parent, J. (2015) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2014.06.002 · Cited by 32
“technologies generally suggests that mobile assessment may be a more efficacious strategy for tracking mood and behavior across a range of disorders and issues than traditional paper diaries (Burke et al., 2012; Shapiro et al., 2008).”
BackgroundResultInfluential
Metadata
- Authors
- Burke, L. E., Styn, M. A., Sereika, S. M., Conroy, M. B., Ye, L., Glanz, K., Sevick, M. A., & Ewing, L. J.
- Affiliations
- University of Pittsburgh (US)
- University of Pennsylvania (US)
- Corresponding author
- Burke, Lora E.
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 43
- Issue
- 1
- Pages
- 20-26
- ISSN
- 0749-3797 (ISSN-L)
- PMC
- PMC3563396
- OpenAlex
- W1901026176
- Semantic Scholar
- 0767d67b987b9a4196d65c0ba79b68df5e9e9f9c
- Cited by
- 295
- References
- 37
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 11 · 2023: 18 · 2022: 20 · 2021: 19 · 2020: 20 · 2019: 22 · 2018: 28 · 2017: 32 · 2016: 33 · 2015: 27 · 2014: 33 · 2013: 16 · 2012: 5
- FWCI
- 46.07
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- Funders
- National Institute of Nursing Research (P30NR003924, P30-NR-03924, K24NR010742)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK071817, R01-DK071817-05S1)
- National Center for Research Resources (5M01-RR-000056, M01RR000056, UL1RR024153)
- 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 › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Keywords
- mobile health, self-monitoring, weight loss, personal digital assistants, personalized feedback, paper diaries, adherence, weight maintenance, obesity, overweight adults, behavioral weight loss, SMART trial, remote feedback, physical activity goals, student retention, weight change over time
- MeSH terms
- Adult, Energy Intake, Diet, Female, Humans, Male, Middle Aged, Pennsylvania, Self Care, Weight Loss, Exercise, Feedback, Psychological, Computers, Handheld, Self Report
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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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
