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
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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
Items 101–200 of 294
Loh, Y. L., Yaw, Q. P., & Lau, Y. (2023) · International Journal of Obesity · doi:10.1038/s41366-023-01304-6 · Cited by 38
Jensen, C. D., Duncombe, K. M., Lott, M., Hunsaker, S. L., Duraccio, K. M., & Woolford, S. J. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6034 · Cited by 32
“Furthermore, Burke et al [15] showed that adults who self-monitored via an electronic device demonstrated superior weight loss (63% achieved 5% weight loss) as compared with those who self-monitored via paper and pencil method (46% achieved 5% weight loss).”
BackgroundMethodResultCan metabotyping help deliver the promise of personalised nutrition?
O’Donovan, C. B., Walsh, M. C., Gibney, M. J., Gibney, E. R., & Brennan, L. (2015) · Proceedings of The Nutrition Society · doi:10.1017/s0029665115002347 · Cited by 31
“intervention groups Burke et al.((22)) 24 month”
Gell, N., Tursi, A., Grover, K. W., & Dittus, K. L. (2019) · Supportive Care in Cancer · doi:10.1007/s00520-019-05038-y · Cited by 31
Li, R., Zhang, Y., Cai, X., Luo, D., Zhou, W., Long, T., Zhang, H., Jiang, H., & Li, M. (2021) · Health Policy · doi:10.1016/j.healthpol.2021.10.010 · Cited by 32
Postrach, E., Aspalter, R. M., Elbelt, U., Koller, M. T., Longin, R., Schulzke, J., & Valentini, L. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2648 · Cited by 29
“Computer-based technology seems to facilitate self-monitoring of diet, and further technical advancements of Web-based programs are expected to continuously improve self-monitoring adherence [36,37].”
BackgroundDoes Journaling Encourage Healthier Choices?
Achananuparp, P., Lim, E., & Abhishek, V. (2018) · International Conference on Digital Health · doi:10.1145/3194658.3194663 · Cited by 29
“That is, individuals who are more active in recording their food journals tend to lose more weights than the less active journalers [9].”
BackgroundBurke, L. E., Sereika, S. M., Parmanto, B., Beatrice, B., Cajita, M., Loar, I., Pulantara, I. W., Wang, Y., Kariuki, J. K., Yu, Y. H., et al. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.105958 · Cited by 29
“Given these early 12-week findings and the expected differences in mean percent weight loss at 6 and 12 months based on the results of our SMART trial [51] (mean percent weight loss for PDA + FB group of −6.”
ResultSelf-citationBurke, L. E., Sereika, S. M., Parmanto, B., Bizhanova, Z., Kariuki, J. K., Cheng, J. S.-C., Beatrice, B., Loar, I., Pulantara, I. W., Wang, Y., et al. (2021) · Obesity · doi:10.1002/oby.23321 · Cited by 28
“The daily, remotely delivered FB messages enhanced adherence and improved weight loss, suggesting that technology can improve outcomes (13).”
BackgroundSelf-citationBurke, L. E., Sereika, S. M., Bizhanova, Z., Parmanto, B., Kariuki, J. K., Cheng, J. S.-C., Beatrice, B., Cedillo, M., Pulantara, I. W., Wang, Y., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/38243 · Cited by 33
“In SMART, adherence to SM strongly predicted weight loss at all time points [17,29,49].”
Self-citationMobile health architecture for obesity management using sensory and social data
Harous, S., El Menshawy, M., Serhani, M. A., & Benharref, A. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.12.005 · Cited by 28
Increasing mobile health application usage among Generation Z members: evidence from the UTAUT model
Aydın, G. (2023) · International Journal of Pharmaceutical and Healthcare Marketing · doi:10.1108/ijphm-02-2021-0030 · Cited by 40
Daily and Seasonal Influences on Dietary Self-monitoring Using a Smartphone Application
Pellegrini, C. A., Conroy, D. E., Phillips, S. M., Pfammatter, A. F., McFadden, H. G., & Spring, B. J. (2018) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2016.12.004 · Cited by 26
Development of a Smartphone Application for Dietary Self-Monitoring
Ahn, J. S., Kim, D. W., Kim, J., Park, H., & Lee, J. E. (2019) · Frontiers in Nutrition · doi:10.3389/fnut.2019.00149 · Cited by 27
Zhang, Y., Fang, Y., Xu, Y., Xiong, P., Zhang, J., Yang, J., Li, R., & Tan, X. (2020) · Brain and Behavior · doi:10.1002/brb3.1599 · Cited by 28
“As Burke et al. (2012) ranked “identifying strategies that improve self-monitoring adherence” as an important factor in moving forward for mHealth-based weight loss efforts.”
BackgroundUsing self-monitoring technology for nutritional counseling and weight management
Carpenter, C. A., Ugwoaba, U. A., Cardel, M. I., & Ross, K. M. (2022) · Digital Health · doi:10.1177/20552076221102774 · Cited by 33
The effect of self-monitoring limited social media use on psychological well-being.
Faulhaber, M. E., Lee, J. E., & Gentile, D. A. (2023) · Technology Mind and Behavior · doi:10.1037/tmb0000111 · Cited by 27
Fitzpatrick, S. L., Bandeen‐Roche, K., Stevens, V. J., Coughlin, J. W., Rubin, R. R., Brantley, P. J., Funk, K., Svetkey, L. P., Jerome, G. J., Dalcin, A., et al. (2013) · Obesity · doi:10.1002/oby.20636 · Cited by 26
Evaluating User Needs in Wellness Apps
Platt, A. J., Outlay, C. N., Sarkar, P., & Karnes, S. (2015) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2015.1099803 · Cited by 26
“Although there exists a wide body of work in the domain of mHealth, we exclude from our discussion papers that focus on web-based tools (e.g., Yeratziotis, Pottas, & Van Greunen, 2012) and those that do not address features desired by users (e.g., Burke et al., 2012; Seto, 2013).”
BackgroundAdherence to mobile‐app‐based dietary self‐monitoring—Impact on weight loss in adults
Payne, J. E., Turk, M. T., Kalarchian, M. A., & Pellegrini, C. A. (2021) · Obesity Science & Practice · doi:10.1002/osp4.566 · Cited by 29
“Regarding completeness, individuals who recorded 50% or more of their weekly calorie goal were considered adherent, while those who recorded less than 50% of their goal were non‐ adherent.(17) Considering the lack of consensus on assessment of adherence,(10,11) and that the DSM adherence criteria associated with weigh…”
BackgroundMethodInfluentialBudge, E. J., Tsoti, S. M., Howgate, D. J., Sivakumar, S., & Jalali, M. (2015) · Annals of Medicine · doi:10.3109/07853890.2015.1091945 · Cited by 26
“Personalized health care applications which support lifestyle changes such as smoking cessation (19), weight loss (20), exercise programmes, and the management of chronic pain disorders are gaining popularity (21).”
BackgroundCan mHealth Revolutionize the Way We Manage Adult Obesity?
Bhardwaj, N. N., Wodajo, B., Gochipathala, K., Paul, D. P., & Coustasse, A. (2017) · Europe PMC (PubMed Central) · Cited by 25
MyHealthyGut: development of a theory-based self-regulatory app to effectively manage celiac disease
Dowd, A. J., Jackson, C., Tang, K. T. Y., Nielsen, D., Clarkin, D. H., & Culos-Reed, S. N. (2018) · mHealth · doi:10.21037/mhealth.2018.05.05 · Cited by 25
“, smartphones) to self-monitor behaviours and promote health and fitness is gaining empirical evidence as an effective and efficient means to assist in self-monitoring dietary behaviours and disease symptoms (20-22,25,26).”
BackgroundDefining Adherence to Dietary Self-Monitoring Using a Mobile App: A Narrative Review
Payne, J. E., Turk, M. T., Kalarchian, M. A., & Pellegrini, C. A. (2018) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2018.05.011 · Cited by 26
A smartphone-enhanced pill-dispenser providing patient identification and in-take recognition
Crema, C., Depari, A., Flammini, A., Lavarini, M., Sisinni, E., & Vezzoli, A. (2015) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2015.7145252 · Cited by 24
“In the past, for instance, the use of mHealth was suggested to facilitate the self-monitoring for weight loss, which is traditionally performed with paper diaries [6].”
MethodTurner-McGrievy, G. M., Yang, C.-H., Monroe, C., Pellegrini, C., & West, D. S. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00203-9 · Cited by 22
“Participants in previous studies have indicated that dietary self-monitoring is burdensome (Burke et al., 2009) and mHealth technology has held promise as a way to reduce the burden of diet tracking (Burke et al., 2012).”
BackgroundDowd, A. J., Warbeck, C. B., Tang, K. T. Y., Fung, T. S., & Culos-Reed, S. N. (2020) · Digital Health · doi:10.1177/2055207620903627 · Cited by 22
Carpenter, C. A., Eastman, A., & Ross, K. M. (2022) · JMIR Formative Research · doi:10.2196/33603 · Cited by 26
Kunstler, B., Cook, J. L., Kemp, J. L., O’Halloran, P., & Finch, C. F. (2018) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2018.06.002 · Cited by 22
Enyioha, C., Hall, M., Voisin, C. E., & Jonas, D. E. (2022) · JMIR Public Health and Surveillance · doi:10.2196/25890 · Cited by 24
“Changes in diet and activity patterns during holiday periods, the challenge or burden of adherence to self-monitoring over a long period of time, and the need for coaching in addition to self-monitoring have been suggested as reasons for the difficulty in maintaining the long-term effects of mHealth interventions for…”
BackgroundBirukila, G., Sufiyan, M. B., Epstein, H., Gugong, V., Anger, R., Corkum, M., Nebanat, A. J., Musoke, F., & Alabi, O. (2016) · Global Public Health · doi:10.1080/17441692.2016.1152283 · Cited by 22
“…(Kazi, Khoja, Ali, & Ali, 2012), encourage smoking cessation in New Zealand (Whittaker et al., 2008), help people monitor weight loss in the USA (Burke et al., 2012; Steinberg, Levine, Askew, Foley, & Bennett, 2013) and help health workers track diabetes patients in Nigeria (Okoro, Sholagberu,…”
BackgroundmHealth Tools for the Pediatric Patient-Centered Medical Home
Slaper, M. R., & Conkol, K. J. (2014) · Pediatric Annals · doi:10.3928/00904481-20140127-09 · Cited by 20
Schumacher, L. M., Martinelli, M., Convertino, A. D., Forman, E. M., & Butryn, M. L. (2020) · Annals of Behavioral Medicine · doi:10.1093/abm/kaaa034 · Cited by 22
“Consistent self-monitoring of eating behavior, physical activity, and body weight is integral to success in behavioral weight loss (BWL) programs [1, 2], yet individuals in these programs may struggle to consistently selfmonitor [3, 4].”
BackgroundNezami, B. T., Hurley, L., Power, J. M., Valle, C. G., & Tate, D. F. (2022) · Obesity · doi:10.1002/oby.23377 · Cited by 24
“There is evidence that digital calorie monitoring can improve adherence rates compared with paper monitoring in highintensity behavioral programs (17,20,25), and it has been associated with weight loss in web- and mobile- based programs (26- 28).”
BackgroundSalahshoornezhad, S., Sohrabi, Z., Mani, A., Abdelbasset, W. K., Mehrabi, M., Zare, M., Mehrabani, S., Gerami, S., Haghighat, N., Akbarzadeh, M., et al. (2022) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2022.04.014 · Cited by 20
Engagement with eHealth Self-Monitoring in a Primary Care-Based Weight Management Intervention
Wolin, K. Y., Steinberg, D., Lane, I. B., Askew, S., Greaney, M. L., Colditz, G. A., & Bennett, G. G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140455 · Cited by 24
“Previous studies have shown that mobile phone based self-monitoring may improve adherence [12, 17, 22].”
BackgroundGetting more people on the stairs: The impact of point-of-decision prompts
Allais, O., Bazoche, P., & Teyssier, S. (2017) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.09.006 · Cited by 21
Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment
Springer, A., Venkatakrishnan, A., Mohan, S., Nelson, L., Silva, M., & Pirolli, P. L. T. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9151 · Cited by 21
“Considering the effectiveness of self-affirmation and the increasing deliverance of interventions through mobile phones [22,23], there is a need to adapt self-affirmation exercises to the mobile medium that do not require long writing and reading tasks.”
BackgroundMalaeb, S., Harindhanavudhi, T., DIETSCHE, K. B., Esch, N., Manoogian, E. N. C., Panda, S., Mashek, D. G., Wang, Q., & Chow, L. S. (2020) · Nutrients · doi:10.3390/nu12113396 · Cited by 21
“Moreover, technology-based mobile food records facilitate and improve self-monitoring adherence and weight loss [23,24], as they can capture food records prospectively rather than retrospectively.”
BackgroundTanenbaum, M. L., Ross, K. M., & Wing, R. R. (2016) · Obesity · doi:10.1002/oby.21650 · Cited by 20
“Thus, frequent self-monitoring of weight, in addition to monitoring of physical activities and eating behaviors, is crucial for success in weight management (9-13).”
Challenges in conducting mHealth research with underserved populations: Lessons learned
Nelson, L. A., & Zamora‐Kapoor, A. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15609853 · Cited by 19
“The success of such interventions depends critically on ensuring that participants adhere to the use of a specific weight management application.(11,12) These findings encouraged us to design an intervention to increase adherence to the use of Myfitnesspal, which recent research has shown to be the most popular smartp…”
BackgroundEisenberg, M. H., Phillips, L. A., Fowler, L., & Moore, P. J. (2017) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2017.01.008 · Cited by 20
“…reducing the amount of feedback provided to participants, potentially by turning the face of the accelerometer away from participants or using devices that allow researchers to remove the feedback may further decrease the influence of the Fitbit on participants' behavior (Burke et al., 2012).”
BackgroundInfluentialStahl, S. T., Emanuel, J. E., Albert, S. M., Dew, M. A., Schulz, R., Robbins-Welty, G. A., & Reynolds, C. F. (2017) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2017.09.002 · Cited by 19
“We implemented a technology-based approach using personalized tablets to decrease participant burden and improve participant retention [22,23].”
MethodEfficacy of a Commercial Weight Management Program Compared With a Do-It-Yourself Approach
Tate, D. F., Lutes, L. D., Bryant, M., Truesdale, K. P., Hatley, K. E., Griffiths, Z., Tang, T. S., Padgett, L., Pinto, A. M., Stevens, J., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.26561 · Cited by 21
Mexican Origin Hispanic Men’s Perspectives of Physical Activity–Related Health Behaviors
Valdez, L. A., Morrill, K. E., Griffith, D. M., Lindberg, N. M., Hooker, S. P., & Garcia, D. O. (2019) · American Journal of Men s Health · doi:10.1177/1557988319834112 · Cited by 18
“Research implies that self-monitoring can be improved with the supplementation of traditional interventions with PA tracking technology (Burke et al., 2011, 2012).”
BackgroundTrajectories of Weight Change and Predictors Over 18‐Month Weight Loss Treatment
Zheng, Y., Sereika, S. M., Danford, C. A., Imes, C. C., Goode, R. W., Mancino, J., & Burke, L. E. (2017) · Journal of Nursing Scholarship · doi:10.1111/jnu.12283 · Cited by 17
Self-citationGanapathy, K., Kanwar, V., Bhatnagar, T., & Uthayakumaran, N. (2016) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0198 · Cited by 17
The Ubiquitous Role of Smartphones in Mobile Health
Appelboom, G. (2014) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2014.01.00004 · Cited by 17
“leading to better health outcomes [8,9].”
Promoting and Sustaining Positive Personal Health Behaviors – Putting the Person First
Teyhen, D. S., Robbins, D. A., & Ryan, B. A. (2018) · Military Medicine · doi:10.1093/milmed/usy212 · Cited by 20
“Health care providers often use interventions that track food, exercise, and sleep behaviors because they are often effective in improving health behavior change.(21,58,86,87) However, time requirements, inability to develop a personal plan, and limited knowledge on how to effectively track caloric intake and expendit…”
BackgroundSittig, S., McGowan, A., & Iyengar, S. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01591-w · Cited by 17
Cohen, D. J., Keller, S., Hayes, G. R., Dorr, D. A., Ash, J. S., & Sittig, D. F. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0804-1 · Cited by 17
“Historically, the most common method for collecting ODLtype data has been the paper diary, which is cumbersome, time consuming, and tedious to complete [7].”
BackgroundMethodSperrin, M., Rushton, H., Dixon, W. G., Normand, A., Villard, J., Chieh, A. Y., & Buchan, I. E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4767 · Cited by 16
“Technologies that enable weight to be captured digitally and fed automatically into consumer health records can enhance both the utilization and effects of self-weighing [8,9].”
Defining a Structure and Methodology for the Practice of Lifestyle Medicine
Egger, G. J. (2016) · American Journal of Lifestyle Medicine · doi:10.1177/1559827616669327 · Cited by 17
Ahn, J. S., Lee, H., Kim, J., Park, H., Kim, D. W., & Lee, J. E. (2020) · JMIR mhealth and uhealth · doi:10.2196/14013 · Cited by 16
Neumeier, W., Guerra, N., Hsieh, K., Thirumalai, M., Ervin, D., & Rimmer, J. (2021) · Disability and Health Journal · doi:10.1016/j.dhjo.2021.101111 · Cited by 13
A systematic review on eHealth technology personalization approaches
ten Klooster, I., Kip, H., van Gemert‐Pijnen, J. E. W. C., Crutzen, R., & Kelders, S. M. (2024) · iScience · doi:10.1016/j.isci.2024.110771 · Cited by 20
Brindal, E., Hendrie, G. A., Freyne, J., & Noakes, M. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7796 · Cited by 15
“Weight monitoring may be most effective when combined with feedback [26].”
Saslow, L. R., Moskowitz, J. T., Mason, A. E., Daubenmier, J. J., Liestenfeltz, B., Missel, A. L., Bayandorian, H., Aikens, J. E., Kim, S., & Hecht, F. M. (2020) · JMIR Diabetes · doi:10.2196/15835 · Cited by 16
How Cultural Norms Influence Persuasive Design: A Study on Chinese Food Journaling Apps
Lu, X., Chen, Y., & A. Epstein, D. (2021) · Designing Interactive Systems Conference 2021 · doi:10.1145/3461778.3462142 · Cited by 15
“For example, food journals sending daily messages giving feedback on weight loss process have been shown to increase engagement and efficiency in weight loss programs over traditional paper diaries [10].”
BackgroundLugones-Sánchez, C., Recio-Rodriguez, J. I., Menéndez-Suárez, M., Saz-Lara, A., Ramírez‐Manent, J. I., Sánchez-Calavera, M. A., Gómez-Sánchez, L., Rodríguez‐Sánchez, E., García-Ortiz, L., & Evident 3 Investigators Group (2022) · Nutrients · doi:10.3390/nu14020270 · Cited by 18
ePSICONUT: An e-Health Programme to Improve Emotional Health and Lifestyle in University Students
Cantisano-Guzmán, L. M., González-Soltero, R., Fernández, A. B., & Belando‐Pedreño, N. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159253 · Cited by 15
“It could be counterproductive for people’s health, as well as ineffective, to access and use these digital platforms without an expert supervision [9].”
BackgroundNew tools for personalised nutrition: The F ood4 M e project
Ryan, N. M., O’Donovan, C. B., Forster, H., Woolhead, C., & Walsh, M. C. (2015) · Nutrition Bulletin · doi:10.1111/nbu.12143 · Cited by 16
Starting the Conversation – A Childhood Obesity Knowledge Project Using an App
Appel, H., Huang, B., Cole, A. M., James, R. D., & Ai, A. L. (2014) · British Journal of Medicine and Medical Research · doi:10.9734/bjmmr/2014/5512 · Cited by 14
“Emerging evidence suggests that smart phone applications are effective in helping adults lose weight through supporting behavior change [24,25].”
BackgroundKulikova, M. S., Gornyi, B., Васильевна, К. А., Karaulova, V. G., Калинина, А. М., & Drapkina, O. M. (2021) · Russian Journal of Preventive Medicine · doi:10.17116/profmed20212410124 · Cited by 14
Burke, L. E., Bizhanova, Z., Conroy, M. B., Cheng, J. S.-C., Beatrice, B., Kariuki, J. K., Parmanto, B., & Sereika, S. M. (2025) · Obesity · doi:10.1002/oby.24234 · Cited by 14
“We defined each measure of adherence based on empirical evidence from earlier trials [17, 18].”
Self-citationDemir, H., & Arslan, E. (2017) · Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi · doi:10.18493/kmusekad.400161 · Cited by 14
Multimodal Telemonitoring for Weight Reduction in Patients With Sleep Apnea
Murase, K., Minami, T., Hamada, S., Gozal, D., Takahashi, N., Nakatsuka, Y., Takeyama, H., Tanizawa, K., Endo, D., Akahoshi, T., et al. (2022) · CHEST Journal · doi:10.1016/j.chest.2022.07.032 · Cited by 14
Results From a Tailored SMS and Behavior-Tracking Pilot Study on Sun-Safe Behaviors in Young Women
Darlow, S. D., & Heckman, C. J. (2017) · Health Education & Behavior · doi:10.1177/1090198117699507 · Cited by 14
“Behavior tracking can easily be utilized with mobile technology and has been shown to be effective in this context (Burke et al., 2012; Turner-McGrievy et al., 2013).”
BackgroundNunns, M., Febrey, S., Abbott, R. A., Buckland, J., Whear, R., Shaw, L., Bethel, A. C., Boddy, K., Coon, J. T., & Meléndez‐Torres, G. J. (2025) · Journal of Medical Internet Research · doi:10.2196/65443 · Cited by 14
Incentives to Create and Sustain Healthy Behaviors: Technology Solutions and Research Needs
Teyhen, D. S., Aldag, M., Centola, D. M., Edinborough, E., Ghannadian, J. D., Haught, A., Jackson, T. N., Kinn, J. T., Kunkler, K. J., Levine, B. A., et al. (2014) · Military Medicine · doi:10.7205/milmed-d-14-00111 · Cited by 15
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“The proliferation of mobile phones and software apps has provided a new channel for health promotion, including symptom recording [7], smoking cessation [8], and weight control [7].”
Understanding Drivers of Dietary Behavior before and during Pregnancy in Industrialized Countries
Malek, L., Umberger, W. J., Zhou, S. J., & Makrides, M. (2014) · Nestlé Nutrition Institute Workshop series · doi:10.1159/000360356 · Cited by 12
“Similar strategies were used previously and were found to improve women’s adherence to dietary self-monitoring, leading to weight loss [64].”
The Use of Mobile Health to Assist Self-management and Access to Services in a Rural Community
Serafica, R. C., Inouye, J., Lukkahatai, N., Braginsky, N., Pacheco, M. Y., & Daub, K. F. (2018) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000494 · Cited by 12
Höchsmann, C., Fearnbach, N., Dorling, J. L., Fazzino, T. L., Myers, C. A., Apolzan, J. W., & Martin, C. K. (2021) · Nutrients · doi:10.3390/nu13103340 · Cited by 12
“This suggests that while mHealth technology certainly holds promise in reducing the burden of dietary self-monitoring [16,17], some mHealth methods are more burdensome than others, and differences in burden are detected by prospective users.”
BackgroundInada, S., Yoshiuchi, K., Iizuka, Y., Ohashi, K., Kikuchi, H., Yamamoto, Y., Kadowaki, T., & Akabayashi, A. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9535-1 · Cited by 12
“[17], which did not use food recording with PDA as an EMI tool, the adherence rate”
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“The results of the reports suggest that daily or weekly feedback and encouragement by sending motivational messages can also promote dietary intake and self‐ efficacy.(23,24)”
BackgroundWang, E.-H., Zhou, L., Chen, S.-H. K., Hill, K. J., & Parmanto, B. (2018) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/rehab.9009 · Cited by 13
“Furthermore, mHealth platforms can empower patients to manage their own diseases [2-12].”
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“Relatedly, although rates of overweight/obesity across men and women are similar (45), the current sample is predominantly female; however, this is common in many weight loss trials.(46-48)”
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“ICT systems also allow for increased monitoring, one of the key elements of managing and reducing body weight [17, 18].”
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Prediction model development of women's daily asthma control using fitness tracker sleep disruption
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Key Enablers to Facilitate Healthy Behavior Change: Workshop Summary
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Self-citationAchieving and Maintaining Cardiovascular Health Across the Lifespan
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A framework for examining the function of digital health technologies for weight management
Azar, K. M. J., Bennett, G. G., Nolting, L. A., Rosas, L. G., Burke, L. E., & Ma, J. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/ibx050 · Cited by 9
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Tracking the sedentary lifestyle using smartphone: A pilot study
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Recommender systems use in weight management mHealth interventions: A scoping review
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Patient-centered financial incentives for health: Can employers get change for their dollars?
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“competence-promoting feedback through peripheral devices with automated technologies.(15,16) For example, devices like the Withings scale and the Fitbit pedometer collect data which can be”
MethodMicro-context recognition of sedentary behaviour using smartphone
Fahim, M., Khattak, A. M., Baker, T., Chow, F., & Shah, B. (2016) · International Conference on Digital Information and Communication Technology and its Applications (DICTAP) · doi:10.1109/dictap.2016.7543996 · Cited by 8
“Several research studies and a large number of lifestyle tracking devices are available [2], [7], [10], [11], [12], [13].”
BackgroundMustafa Kemal University Students' Use of Mobile Health Applications
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“However, dietary self-monitoring differs from dietary assessment(12) in that dietary assessment is infrequent and must be highly accurate, whereas self-monitoring must occur every time something is consumed, as the more proximal the feedback is to the desired behavior, the more likely it is for that behavior to be sus…”
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Aplicaciones para terminales móviles en salud
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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
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