Associations Between Self-Monitoring and Weight Change in Behavioral Weight Loss Interventions
Open accessAbstract
OBJECTIVE: The current study is a secondary analysis of the Live SMART trial, a randomized controlled trial comparing a behavioral weight loss (BWL) condition delivered via smartphone (SMART) to a group-based BWL condition (GROUP) and a control condition (CONTROL). Given the established importance of self-monitoring for weight loss, the aims were to evaluate bidirectional associations between adherence to self-monitoring and weight change and to examine the moderating effect of treatment condition on these associations. METHOD: Adults with overweight/obesity (n = 276; 83% women; 92.8% White; Mage = 55.1 years; Mbody mass index = 35.2 kg/m2) were instructed to self-monitor dietary intake, daily weight, and physical activity minutes via paper diaries in GROUP and CONTROL and via a smartphone application in SMART. All participants were weighed monthly at the research center. Adherence to self-monitoring was assessed via examination of self-monitoring records. RESULTS: Generalized linear mixed models revealed that adherence to self-monitoring of dietary intake, self-weighing, and physical activity for each month was associated with weight change throughout that month, such that increased frequency of self-monitoring led to greater weight loss (ps < .001). For the GROUP condition only, poorer weight losses in 1 month were prospectively associated with poor adherence to self-monitoring the following month (ps ≤ .01). CONCLUSIONS: Results provide evidence of a bidirectional association between self-monitoring and weight change. Better self-monitoring was consistently associated with better weight loss across intervention and tracking modalities. Poorer weight loss was prospectively associated with poorer self-monitoring in group treatment, suggesting that social influences could drive adherence in this form of treatment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Cites
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Claims the paper makes
25 of 44 cited sources checked · 14 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Among BWL participants, burden and boredom hinder accurate and consistent self-monitoring.
“research among BWL participants revealed that burden and boredom are barriers to accurate and consistent self-monitoring”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Burke, Swigart, Warziski Turk, Derro, & Ewing, 2009 source
- Premisecitation
Equivalence testing showed no significant 18-month weight loss differences between smartphone-based BWL (5.5 kg; 95% CI [3.9, 7.0]), in-person BWL (5.9 kg; 95% CI [4.5, 7.4]), and control (6.4 kg; 95% CI [3.7, 9.2]).
“Equivalence testing revealed no significant differences in 18-month weight loss across smartphone-based BWL (5.5 kg; 95% CI [3.9, 7.0]), in-person BWL (5.9 kg; 95% CI [4.5, 7.4]), and the control (6.4 kg; 95% CI [3.7, 9.2])”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
The Results, Primary Outcome (Weight Change) section reports the quoted 18-month weight losses and confidence intervals, with no significant difference between SMART and GROUP and CONTROL indistinguishable from SMART. The Methods, Statistical Approach section describes WGEE analyses and significance-tested pairwise comparisons, not equivalence testing; that characterization of the analysis is unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Premisecitation
Using technology with large food and activity databases can reduce the burden of self-monitoring.
“One way to improve self-monitoring is to make it less burdensome via technology with access to large food databases (including brand names and restaurant items) and physical activities”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Burke, Wang, et al., 2011 source
The Discussion says dietary and exercise software might lessen the burden of self-monitoring, and that an extensive food database including restaurant items avoids nutrient lookups and calculations. The Results also describe physical-activity self-monitoring software. Our copy does not specify brand-name foods or a large database of physical activities, so those details of the citing sentence are not supported. (LLM, conf=0.83)
Experimental — accuracy not yet measured
- Premisecitation
Greater adherence to self-monitoring of exercise, weight, and diet, measured by how often recommended recording occurs, is linked to larger weight losses in BWL programs.
“Adherence to self-monitoring weight-related behaviors (i.e., exercise, weight, diet), as defined by the frequency of recommended recording, is associated with greater weight losses in BWL interventions”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Burke, Wang, & Sevick, 2011 source
- cites Madigan, Daley, Lewis, Aveyard, & Jolly, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Electronic self-monitoring tools have led to better adherence to self-monitoring in BWL than paper-and-pencil tracking.
“Electronic self-monitoring tools, as compared to paper-and-pencil tracking, have produced increased adherence to self-monitoring within BWL”
Quote in the paperSubstantial claim3 of 3 sources checked- cites Carter, Burley, Nykjaer, & Cade, 2013 source
- cites Spring et al., 2013 source
The Intervention (Weight Loss) Phase describes PDA self-monitoring and coaching, but does not compare self-monitoring adherence with paper-and-pencil tracking. The Results section reports weight loss and MOVE! session attendance, not that adherence comparison; searches for self-monitoring adherence and paper-and-pencil recording found no such result in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites Thomas et al., 2017 source
The Results report substantially more frequent body-weight self-monitoring with a smart scale than in the standard program. But the Methods (Study design; page not provided) say the comparison group entered weights through a website or mobile app, not on paper; the study therefore does not establish the claimed electronic-versus-paper comparison. (LLM, conf=0.90)
Experimental — accuracy not yet measured
- Study design and conductcitation
Missing data are likely confounded with an overall pattern of disengagement that could also be related to weight regain observed during the maintenance period.
“Nonetheless, conclusions should be interpreted with caution, as the missing data are likely confounded with an overall pattern of disengagement (Scherer, Ben-Zeev, Li, & Kane, 2017) that could also be related to weight regain observed during the maintenance period (Thomas et al., 2019).”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Scherer, Ben-Zeev, Li, & Kane, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Thomas et al., 2019 source
The Thomas et al. citation is attached to weight regain during the maintenance period, not to the sentence’s claim about missing data and disengagement. The Results (Primary Outcome: Weight Change; page unavailable) report weight loss at 18 months but do not establish whether participants regained weight during a maintenance period. Searches for “regain,” “maintenance,” and weight changes between assessments found no passage confirming that specific point. (LLM, conf=0.70)
Experimental — accuracy not yet measured
- Premiseown data
Self-monitored physical activity did not differ significantly across groups.
“Self-monitored physical activity was not significantly different across groups.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
- Study design and conductcitation
The Live SMART trial had power to examine its primary between-group weight change outcomes.
“The Live SMART trial was powered to examine proposed primary outcomes for between-groups weight change (Thomas et al., 2019).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
- Findingown data
The association between self-weighing and monthly weight loss remained statistically significant when controlling for self-monitoring dietary intake and physical activity frequency.
“This remained statistically significant when controlling for adherence to self-monitoring dietary intake and frequency of monitoring physical activity.”
Quote in the paperSubstantial claim - Findingown data
The association between self-monitoring dietary intake and monthly weight loss became non-significant when controlling for frequency of self-monitoring physical activity and adherence to self-weighing.
“However, when controlling for frequency of self-monitoring physical activity and adherence to self-weighing, the effect of self-monitoring dietary intake became non-significant.”
Quote in the paperSubstantial claim - Findingreasoning
Greater physical activity self-monitoring frequency was associated with more %WL in GROUP and SMART conditions relative to CONTROL.
“One such difference was found; greater physical activity self-monitoring frequency was associated with more %WL in the GROUP and SMART conditions relative to CONTROL.”
Quote in the paperSubstantial claim - Findingown data
The physical activity self-monitoring interaction findings were consistent when controlling for self-monitoring dietary intake and weight.
“These findings were consistent when controlling for adherence to self-monitoring dietary intake and weight.”
Quote in the paperNot a substantial claim - Findingown data
Better self-monitoring was consistently linked to better weight loss across intervention and tracking modalities.
“Better self-monitoring was consistently associated with better weight loss across intervention and tracking modalities.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Findingown data
In GROUP, greater %WL predicted more days of self-monitoring in the subsequent month.
“As seen in Figure 2, greater %WL predicted more days of self-monitoring in the subsequent month in GROUP, while there was no association in CONTROL and SMART.”
Quote in the paperSubstantial claim - Findingown data
Greater frequency of self-monitored physical activity was associated with greater %WL in SMART and GROUP conditions compared to CONTROL.
“An interaction effect was observed on the frequency of self-monitoring physical activity on %WL, such that more days of self-monitored physical activity was associated with greater %WL in SMART and GROUP conditions, compared to the CONTROL condition.”
Quote in the paperSubstantial claim - Findingown data
A fine-grained approach using intensive longitudinal modeling revealed that greater frequency of self-monitoring all three key weight control behaviors was associated with weight change from the start to end of the month.
“However, a fine-grained approach using intensive longitudinal modeling revealed that greater frequency of self-monitoring all three key weight control behaviors was associated with weight change from the start to end of the month.”
Quote in the paperSubstantial claim - Findingown data
Greater weight loss was associated prospectively with increased adherence to self-monitoring in the next month, but only in the GROUP condition.
“Conversely, greater weight loss was associated prospectively with increased adherence to self-monitoring in the next month, albeit only in the GROUP condition.”
Quote in the paperSubstantial claim - Findingown data
There was no interaction effect of condition and frequency of self-monitoring on %WL for self-weighing (p=.26).
“There was no interaction effect of condition × frequency of self-monitoring (p=.26) and, as such, the interaction term was removed from the final model.”
Quote in the paperNot a substantial claim - Findingown data
There was a statistically significant interaction effect of condition and %WL on prospective adherence to self-weighing (B=0.13, SE=0.05, z=2.67, p=.01).
“There were statistically significant interaction effects of condition and %WL on prospective adherence to self-monitoring dietary intake, B=0.16, SE=0.05, z=3.49, p<.001, adherence to self-weighing, B=0.13, SE=0.05, z=2.67, p=.01, and frequency of monitored physical activity, B=0.15, SE=0.05, z=2.83, p=.005”
Quote in the paperSubstantial claim - Findingown data
Greater adherence to self-monitoring dietary intake was associated with greater monthly weight loss during that month.
“A main effect of adherence to self-monitoring dietary intake on %WL was observed, such that more days of self-monitoring corresponded to greater monthly weight losses during that month.”
Quote in the paperSubstantial claim - Findingown data
There was a statistically significant interaction effect of condition and %WL on prospective adherence to self-monitoring dietary intake (B=0.16, SE=0.05, z=3.49, p<.001).
“There were statistically significant interaction effects of condition and %WL on prospective adherence to self-monitoring dietary intake, B=0.16, SE=0.05, z=3.49, p<.001, adherence to self-weighing, B=0.13, SE=0.05, z=2.67, p=.01, and frequency of monitored physical activity, B=0.15, SE=0.05, z=2.83, p=.005”
Quote in the paperSubstantial claim - Findingown data
There was no interaction effect of condition and frequency of self-monitoring on %WL for dietary intake (p=.29).
“There was no interaction effect of condition × frequency of self-monitoring (p=.29) and, as such, the interaction term was removed from the final model reported in Table 2.”
Quote in the paperNot a substantial claim - Findingown data
Only in the GROUP condition, poorer weight loss in one month predicted poorer adherence to self-monitoring in the next month (ps≤.01).
“For the GROUP condition only, poorer weight losses in one month were prospectively associated with poor adherence to self-monitoring the following month (ps≤.01).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Findingown data
Poorer weight loss prospectively predicted poorer self-monitoring in group treatment.
“Poorer weight loss was prospectively associated with poorer self-monitoring in group treatment”
Quote in the paperSubstantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Findingown data
In generalized linear mixed models, adherence to self-monitoring of dietary intake, self-weighing, and physical activity in a given month was associated with weight change during that month, with greater self-monitoring frequency predicting greater weight loss (ps<.001).
“Generalized linear mixed models revealed that adherence to self-monitoring of dietary intake, self-weighing, and physical activity for each month was associated with weight change throughout that month, such that increased frequency of self-monitoring led to greater weight loss (ps<.001).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Findingown data
The effect of self-monitoring dietary intake on monthly weight losses became non-significant while controlling for frequency of monitoring weight and physical activity.
“While the pattern of findings remained the same for self-monitoring weight and physical activity, the effect of self-monitoring dietary intake on monthly weight losses became non-significant while controlling for the frequency of monitoring weight and physical activity.”
Quote in the paperSubstantial claim - Findingown data
There was a statistically significant interaction effect of condition and %WL on prospective frequency of monitored physical activity (B=0.15, SE=0.05, z=2.83, p=.005).
“There were statistically significant interaction effects of condition and %WL on prospective adherence to self-monitoring dietary intake, B=0.16, SE=0.05, z=3.49, p<.001, adherence to self-weighing, B=0.13, SE=0.05, z=2.67, p=.01, and frequency of monitored physical activity, B=0.15, SE=0.05, z=2.83, p=.005”
Quote in the paperSubstantial claim - Findingown data
Greater adherence to self-monitoring key weight control behaviors (dietary intake, self-weighing, physical activity frequency) was associated with greater %WL across treatment conditions.
“This secondary analysis of the Live SMART trial found that greater adherence to self-monitoring key weight control behaviors (i.e., dietary intake, self-weighing, and frequency of physical activity) was associated with greater %WL across treatment conditions.”
Quote in the paperSubstantial claim - Findingown data
In CONTROL and SMART, there was no association between %WL and self-monitoring in the subsequent month.
“As seen in Figure 2, greater %WL predicted more days of self-monitoring in the subsequent month in GROUP, while there was no association in CONTROL and SMART.”
Quote in the paperSubstantial claim - Findingown data
Greater adherence to self-weighing was associated with greater monthly weight loss.
“A main effect of adherence to self-monitoring weight on %WL was observed: additional days of self-weighing corresponded to more monthly weight loss.”
Quote in the paperSubstantial claim - Findingown data
Treatment and tracking modality did not result in differential patterns of association between self-monitoring and weight change.
“This study demonstrated that treatment and tracking modality did not result in differential patterns of association between self-monitoring and weight change, but future research could explore factors that may impact the strength of the association.”
Quote in the paperSubstantial claim - Premisecitation
To affect the target behavior, self-monitoring requires recording relevant information promptly, honestly, and regularly.
“For self-monitoring to impact the target behavior, one must record the relevant information in a timely, truthful, and consistent manner”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bandura, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
This is the first study to provide evidence that the association between self-monitoring and weight change could be bidirectional, particularly in intensive group-based treatment.
“This is the first study to provide evidence that the association between self-monitoring and weight change could be bidirectional, particularly in intensive group-based treatment.”
Quote in the paperSubstantial claim - Interpretationcitation
It is likely that adherence behaviors have shared variance and clustering because they are behavioral representations of similar constructs such as motivation.
“For example, it is likely that adherence behaviors have shared variance and clustering given that they are behavioral representations of similar constructs such as motivation (Webber et al., 2010).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Webber et al., 2010 source
- Study design and conductreasoning
Frequency of physical activity self-monitoring was confounded with performance of physical activity because participants were not asked to record zero physical activity minutes on days none was performed.
“However, the frequency of physical activity self-monitoring was confounded with performance of physical activity (since participants were not asked to record an entry of 0 physical activity minutes on days that none was performed).”
- Also stated in: § Discussion · plain
“Finally, adherence to self-monitoring physical activity was confounded with actual performance of physical activity, which represents an interesting distinction between self-monitoring used for clinical purposes as opposed to an outcome of interest within”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Study design and conductcitation
The chosen analytic approach, linear mixed models, is robust to missing data.
“Importantly, the chosen analytic approach, linear mixed models, is robust to missing data.”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
The difference in intervention frequency and intensity likely contributed to the different pattern of association in GROUP versus SMART and CONTROL.
“This difference in intervention frequency and intensity likely contributed to the different pattern of association in GROUP versus SMART and CONTROL.”
Quote in the paperSubstantial claim - Interpretationreasoning
Social influences may drive adherence in group treatment.
“suggesting that social influences could drive adherence in this form of treatment”
Quote in the paperSubstantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Interpretationown data
The results support a bidirectional association between self-monitoring and weight change.
“Results provide evidence of a bidirectional association between self-monitoring and weight change.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
- Interpretationown data
Low-cost technology-based programs can achieve results equivalent to intensive in-person programs.
“indicating that low-cost technology-based programs can produce equivalent results to intensive in-person programs”
Quote in the paperSubstantial claim - Interpretationown data
The association between self-monitoring and weight change is granular, meaning meaningful over shorter time scales like one month, when examined over an entire long-term BWL.
“These novel findings illustrate that the association between self-monitoring and weight change is granular (i.e., meaningful over shorter time scales like one month) when examined over an entire long-term BWL (Riley, 2017).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Riley, 2017 source
- May combine two claims. 18
- Classified as probably not a substantial claim. 25
May combine two claims.
PremisecitationFeatures like saved meals, snacks, and exercise routines make monitoring easier and support longer-term use and adherence.
“Many programs allow for saved meals, snacks, and exercise routines to increase ease of monitoring, which enhances long-term use and adherence”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Peng, Kanthawala, Yuan, & Hussain, 2016 source
The Results section, “Tracking for Awareness and Progress” (page unavailable), says apps log diet and exercise and that participants valued tracking their progress. It does not describe saved meals, snacks, or exercise routines as making monitoring easier or improving long-term adherence. It also reports that some participants expected to stop tracking once they had learned their routines. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBehavioral weight loss (BWL) is the leading non-surgical treatment for weight loss.
“behavioral weight loss (BWL) is the gold-standard non-surgical treatment for weight loss”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites Hales, Fryar, Carroll, Freedman, & Ogden, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Wing et al., 2011 source
The Research Design and Methods > Interventions section describes a structured behavioral weight-control intervention using diet and physical activity. The paper therefore supports behavioral weight loss as a nonsurgical approach, but it does not describe it as the leading or gold-standard nonsurgical treatment. (LLM, conf=0.83)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationThe Live SMART trial was among the first to directly test whether smartphone-based BWL could achieve weight losses at least as good as gold-standard group treatment and to compare both with a control.
“The recent randomized controlled Live SMART trial (Thomas, Bond, Raynor, Papandonatos, & Wing, 2019), was one of the first investigations to directly assess if smartphone-based BWL intervention could produce weight losses at least as good as gold-standard group treatment, and compare both treatments to a control condition.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Thomas, Bond, Raynor, Papandonatos, & Wing, 2019 source
Classified as probably not a substantial claim.
PremisecitationSelf-monitoring is believed to promote the diet and activity changes needed for weight loss by increasing accountability and awareness of one's actions relative to long-term goals.
“Self-monitoring is thought to facilitate the necessary diet and activity changes for weight loss through increased accountability, and enhanced awareness of one’s own actions and how these actions align with a longer-term goal”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Bandura, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Khaylis, Yiaslas, Bergstrom, & Gore-Felton, 2010 source
The SELF-MONITORING section describes tracking diet and physical activity as important to weight loss. SELF-MONITORING AND TECHNOLOGY suggests that accountability to another person may sustain behavioral change. The copy does not specifically say that self-monitoring works by making people aware of how their actions align with a longer-term goal. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationIn smartphone-based BWL, participants using electronic self-monitoring tracked dietary intake more often (37.9% days monitored; 95% CI [32.6, 43.2]) than group-based BWL participants using paper-and-pencil self-monitoring (27.5%; 95% CI [23.6, 31.4]); neither group differed significantly from control (32.0%; 95% CI [24.0, 40.0]).
“Participants in smartphone-based BWL using electronic self-monitoring more frequently tracked their dietary intake (37.9% days monitored; 95% CI [32.6, 43.2]) than group-based BWL with paper-and-pencil self-monitoring (27.5%; 95% CI [23.6, 31.4]); neither differed significantly from the control condition (32.0%; 95% CI [24.0, 40.0]).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
May combine two claims.
InterpretationcitationThis may have led participants to avoid self-monitoring, which might remind them of poor outcomes and associated unpleasant emotions.
“This may have led participants to avoid self-monitoring, which might remind them of poor outcomes and associated unpleasant emotions (Tanenbaum et al., 2016).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Tanenbaum et al., 2016 source
Classified as probably not a substantial claim.
Study design and conductcitationFor frequency of self-monitoring physical activity, a zero-inflated negative binomial distribution with logit link was used to account for multiple factors contributing to zero values.
“For frequency of self-monitoring physical activity, a zero-inflated negative binomial distribution with logit link function was used to account for multiple factors contributing to zero values (i.e., no physical activity v. non-adherence to monitoring physical activity; Atkins et al., 2013).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Atkins et al., 2013 source
May combine two claims.
InterpretationcitationThis result is counterintuitive given the established impact of self-monitoring eating behavior on weight change and is representative of limitations inherent in assessing adherence to closely related self-monitoring health behaviors.
“This result is counterintuitive given the established impact of self-monitoring eating behavior on weight change (Burke, Wang, et al., 2011) and is representative of the limitations inherent in assessing adherence to self-monitoring health behaviors that are closely related.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Burke, Wang, et al., 2011 source
May combine two claims.
PremisecitationThe primary outcomes of Live SMART demonstrated that SMART engaged in more self-monitoring but did not demonstrate greater weight losses.
“The primary outcomes of Live SMART demonstrated that SMART engaged in more self-monitoring but did not demonstrate greater weight losses.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
Classified as probably not a substantial claim.
Study design and conductcitationAdherence to dietary self-monitoring was defined as the number of days per week a participant recorded at least 50% of the daily calorie goal or at least three separate eating occasions.
“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”
Quote in the paperSubstantial claim1 of 2 sources checked- cites Burke et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Thomas & Wing, 2013 source
The Methods > Measures passage gives the same dietary thresholds: at least three meals or food equaling at least 50% of the daily calorie goal. But it also requires participants to record their weight on an adherent day; the citing sentence omits that requirement and describes adherence as dietary self-monitoring alone. (LLM, conf=0.88)
Experimental — accuracy not yet measured
May combine two claims.
Premiseown dataSelf-weighing rates were lower in group-based BWL (21.2%; 95% CI [17.9, 25.9]) than in smartphone-based BWL (30.7%; 95% CI [26.2, 37.2]) and control (29.7%; 95% CI [21.7, 37.7]).
“Self-weighing rates were also lower in the group-based BWL (21.2%; 95% CI [17.9, 25.9]) compared to smartphone-based BWL (30.7%; 95% CI [26.2, 37.2] and control [29.7%; 95% CI [21.7, 37.7].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Thomas et al., 2019 source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationAlthough the current finding requires replication, it suggests that interventionists delivering in-person, group-based treatments should be aware that minor month-to-month weight loss failures can have problematic consequences.
“Although the current finding requires replication, it suggests that interventionists delivering in-person, group-based treatments should be aware that minor (month-to-month) weight loss failures can have problematic consequences (Schumacher et al., 2016).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Schumacher et al., 2016 source
The Discussion reports that more frequent small weight gains were associated with less weight loss and advises clinicians to consider small gains when deciding whether to intervene. Methods > Procedures describes monthly sessions in the maintenance phase, supporting the month-to-month framing. Our copy does not establish that the treatment was in-person and group-based, so it does not support that specific setting. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationAdherence to self-monitoring could be used to identify individuals who need additional intervention to prevent weight regain.
“Also, adherence to self-monitoring could be used to identify individuals who need additional intervention to prevent weight regain (Laitner, Minski, & Perri, 2016).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Laitner, Minski, & Perri, 2016 source
The Discussion reports that, during extended care, participants with the highest food-record completion continued to lose weight, while the less successful groups completed fewer records. It also recommends identifying factors affecting lower-success participants. The paper does not establish that self-monitoring adherence can identify individuals who need additional intervention before weight regain; its analyses are correlational. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim. May combine two claims.
PremisecitationPrevious studies showed that merely adding tracking tools to BWL may be of no benefit, or even have iatrogenic effects.
“Given previous studies showing that merely adding tracking tools to BWL may be of no benefit, or even have iatrogenic effects, use of these tools would have to be accompanied by a careful intervention strategy to guide participants in summarizing and interpreting their data to inform behavior change (Jakicic et al., 2016; Thomas et al., 2017).”
Quote in the paperSubstantial claim2 of 2 sources checkedMay combine two claims.
FindingreasoningContrary to hypotheses, the prospective association between %WL and subsequent self-monitoring was found only in the GROUP condition, though it was present for all three behaviors.
“Contrary to hypotheses, this association was found only in the GROUP condition, though it was present for all three behaviors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe sample was mostly white, middle-aged, and female.
“The sample was mostly white, middle-aged, and female, but obesity affects people of all backgrounds (Wang & Beydoun, 2007).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationAdherence to self-monitoring and attendance at clinic weigh-ins decreased during the maintenance phase.
“Consistent with previous trials (Burke et al., 2012), adherence to self-monitoring and attendance at clinic weigh-ins decreased during the maintenance phase.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Burke et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- The authors state this as an assumption: Participants who did not submit self-monitoring records were assumed to have conducted no self-monitoring.
Classified as probably not a substantial claim.
PremisecitationTracking daily diet, activity, and weight is a central BWL component used to monitor adherence and progress.
“A cornerstone strategy of BWL intervention is self-monitoring of daily dietary intake, physical activity, and weight to track adherence to BWL recommendations and associated progress”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Foster, Makris, & Bailer, 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
Study design and conductcitationMissing weight data were not imputed because the mixed models with a random subject effect accommodate multiple missing data points.
“Missing weights were not imputed because the proposed analyses accommodate multiple missing data points by including a random effect of subject (Edwards, 2000; Krueger & Tian, 2004).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Edwards, 2000
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Krueger & Tian, 2004 source
Classified as probably not a substantial claim.
Study design and conductreasoningAll models controlled for body mass index, age, sex, income, education, and race/ethnicity.
“All models controlled for body mass index, age, sex, income, education, and race/ethnicity, as these variables have been correlated with weight change in previous investigations.”
Quote in the paperSubstantial claim- Rests on an assumption: Body mass index, age, sex, income, education, and race/ethnicity have been correlated with weight change in previous investigations.
Classified as probably not a substantial claim.
Study design and conductreasoningCalculating %WL using baseline weight as the reference ensured that the within-person criterion for comparing the magnitude of %WL stayed constant over time.
“This procedure ensured that the within-person criterion for comparing the magnitude of % WL stayed constant over time.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningGROUP participants with poor weight loss might have experienced more frequent and/or intense feelings of shame due to concerns about fellow participants' and interventionists' perceptions.
“Conversely, GROUP participants with poor weight loss might have experienced more frequent and/or intense feelings of shame due to concerns about fellow participants’ and interventionists’ perceptions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe GROUP format may have led patients with good weight loss outcomes to receive more frequent or intense praise or other reinforcement for self-monitoring adherence.
“For example, the GROUP format may have led patients with good weight loss outcomes to receive more frequent or intense praise or other reinforcement for self-monitoring adherence (Bandura, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Bandura, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown dataOn average, the smartphone-based BWL group self-monitored more, but weight losses did not differ between groups.
“On average, the smartphone-based BWL groupengaged in more self-monitoring, but between-groups weight losses did not differ.”
Quote in the paperSubstantial claimMay combine two claims.
Premisenone givenCompared to the other two conditions, GROUP was characterized by more frequent in-person sessions, which were more intensive than the SMART and CONTROL weigh-ins.
“Compared to the other two conditions, GROUP was characterized by more frequent in-person sessions, which were more intensive than the SMART and CONTROL weigh-ins.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe current study benefited from repeated measurement of weight and adherence to self-monitoring, which allowed for a granular examination of the association between self-monitoring and weight change.
“The current study benefited from repeated measurement of weight and adherence to self-monitoring, which allowed for a granular examination of the association between self-monitoring and weight change.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningIn Aim 1 models, the other self-monitoring behaviors were controlled for to account for underlying characteristics such as motivation that may govern general tendency to adhere.
“To account for underlying characteristics that may govern the general tendency to adhere to self-monitoring recommendations (i.e., motivation), we then controlled for the other self-monitoring behaviors (e.g., the effect of adherence to self-monitoring dietary intake controlling for the effect of self-monitored physical activity and weight).”
Quote in the paperSubstantial claim- Rests on an assumption: Self-monitoring behaviors are jointly prescribed in behavioral weight loss interventions.
May combine two claims.
Premisenone givenGROUP and SMART received active BWL with increasing exercise goals over time, while CONTROL did not receive such a physical activity intervention.
“GROUP and SMART received active BWL, with increasing exercise goals over time. CONTROL did not receive such a physical activity intervention.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenWeight was measured to the nearest 0.1 pounds on a calibrated scale, with participants in light clothing and no shoes.
“Weight was measured to the nearest 0.1 pounds (lbs.) on a calibrated scale in light clothing, without shoes.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThis study replicates and extends the literature by examining the month-to-month association between self-monitoring and weight change over the full weight loss attempt.
“As such, this study replicates and extends the literature by examining the month-to-month association between self-monitoring and weight change over the full weight loss attempt.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Study design and conductnone givenExamining the differences in patterns of associations across treatment and tracking modalities is a major strength.
“Lastly, examining the differences in patterns of associations across treatment and tracking modalities is a major strength.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe results of the current study can inform further refinement of both in-person and smartphone-based BWL interventions.
“The results of the current study can inform further refinement of both in-person and smartphone-based BWL interventions.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe control group lost more weight than expected, which supports the idea that self-monitoring is a powerful weight loss tool even without other intervention components.
“Control outperformed expectations, reinforcing the conclusion that self-monitoring is a powerful tool for weight loss, even in the absence of other intervention components.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductcitationFor adherence to self-monitoring dietary intake and weight, a negative binomial distribution with logit link was used because the count data were positively skewed and the variance often exceeded the mean.
“For adherence to self-monitoring dietary intake and weight, a negative binomial distribution with logit link function was used because these count data were positively skewed, and the variance often exceeded the mean (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataRandomization used a computer algorithm with blocks of five, stratified by gender and cohort.
“Participants were randomized via computer algorithm utilizing blocks of five and stratified by gender and cohort.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataSelf-monitoring adherence may be an important target for timely intervention throughout BWL.
“Thus, self-monitoring adherence may be an important target for timely intervention throughout BWL.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe group meetings' format and content closely followed other empirically supported standard behavioral obesity treatments.
“Format and content of the group meetings closely followed other empirically-supported standard behavioral obesity treatments”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Diabetes Prevention Program Research Group, 2002
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Look AHEAD Research Group, 2006
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
Study design and conductreasoningMonthly clinic visit weights were used because they were the smallest unit measured uniformly across study conditions.
“Monthly clinic visit weights were used for analysis in the current study because this was the smallest unit that was measured uniformly across study conditions (participants in the CONTROL and SMART conditions were not instructed to visit the clinic for weekly weights as in the first 6 months of GROUP condition).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe physical activity self-monitoring finding is consistent with more GROUP and SMART participants engaging in physical activity, which contributed to increased weight loss for those participants.
“Thus, this finding is consistent with more GROUP and SMART participants engaging in physical activity, which contributed to increased weight loss for those participants (Jeffery, Wing, Sherwood, & Tate, 2003).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Jeffery, Wing, Sherwood, & Tate, 2003 source
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataContinuing to assess and intervene on adherence to self-monitoring would be a useful endeavor for enhancing weight loss and weight maintenance in several types of BWL programs.
“Overall, results of the current study indicate that continuing to assess and intervene on adherence to self-monitoring would be a useful endeavor for enhancing weight loss and weight maintenance in several types of BWL programs.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Few studies have directly compared primarily technology-based approaches to gold-standard group-based BWL with paper-and-pencil records.
“However virtually no studies have directly compared primarily technology-based approaches (i.e., online BWL interventions with electronic self-monitoring) to gold-standard group-based BWL with paper-and-pencil records.”
The paper relies on this without support.
The direct association between self-monitoring and weight change, and the moderating effect of treatment condition, had not been evaluated in Live SMART.
“Importantly, the direct association between self-monitoring and weight change, and the moderating effect of treatment condition, has not been evaluated in Live SMART.”
The paper relies on this without support.
Earlier work has mostly taken the self-monitoring–weight change link to be one-directional.
“Moreover, prior research has largely assumed a unidirectional relationship between self-monitoring and weight change”
The authors state this as an assumption.
Participants who missed clinic visits or did not submit a record were assumed to have zero days of self-monitoring.
“Participants who did not attend clinic visits or did not turn in a record were assumed to have zero days of self-monitoring.”
The authors state this as an assumption.
Participants who did not submit self-monitoring records were assumed to have conducted no self-monitoring.
“Further, assuming that participants who did not submit records conducted no self-monitoring could have introduced some degree of bias, especially for the GROUP and CONTROL conditions who were required to submit their records in-person or via mail, as opposed to the records of SMART, which were retrieved electronically and thus required no effort to submit.”
The paper relies on this without support.
Body mass index, age, sex, income, education, and race/ethnicity have been correlated with weight change in previous investigations.
“All models controlled for body mass index, age, sex, income, education, and race/ethnicity, as these variables have been correlated with weight change in previous investigations.”
The paper relies on this without support.
Self-monitoring behaviors are jointly prescribed in behavioral weight loss interventions.
“Since self-monitoring behaviors are jointly prescribed in BWL, this study examined the effect of self-monitoring each behavior (e.g., diet) while controlling for adherence to self-monitoring the other behaviors (e.g., self-weighing, physical activity).”
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?
Mobile applications can help to accomplish a moderate short-term weight loss.
“Goldstein et al. 84 and Muralidharan et al. 85 demonstrated in 2019 that mobile applications can help to accomplish a moderate short-term weight loss.”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentz = 3.49reported p < .001 · recomputed p < .001
“z=3.49, p < .001”
- Consistentz = 2.67reported p = .01 · recomputed p = .008
“z=2.67, p = .01”
- Consistentz = 2.83reported p = .005 · recomputed p = .005
“z=2.83, p = .005”
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.
Final summary overlooks a treatment-dependent association
MajorThe final discussion says treatment and tracking modality did not produce different patterns of association. Earlier results report that the association between physical-activity self-monitoring and weight loss differed by treatment condition.
“This study demonstrated that treatment and tracking modality did not result in differential patterns of association between self-monitoring and weight change”
“An interaction effect was observed on the frequency of self-monitoring physical activity on %WL, such that more days of self-monitored physical activity was associated with greater %WL in SMART and GROUP conditions, compared to the CONTROL condition.”
Final summary denies reported treatment-condition interactions
MajorThe final summary says treatment and tracking modality did not produce different association patterns, but the results report significant interactions between treatment condition and prior weight loss for all three subsequent self-monitoring measures.
“This study demonstrated that treatment and tracking modality did not result in differential patterns of association between self-monitoring and weight change”
“There were statistically significant interaction effects of condition and %WL on prospective adherence to self-monitoring dietary intake, B=0.16, SE=0.05, z=3.49, p<.001, adherence to self-weighing, B=0.13, SE=0.05, z=2.67, p=.01, and frequency of monitored physical activity, B=0.15, SE=0.05, z=2.83, p=.005”
Cites
Wang, Y., & Beydoun, M. A. (2007) · Epidemiologic Reviews · doi:10.1093/epirev/mxm007 · Cited by 2,779 · cited 1× · see citing works
“The sample was mostly white, middle-aged, and female, but obesity affects people of all backgrounds (Wang & Beydoun, 2007).”
BackgroundRevision of the Physical Activity Readiness Questionnaire (PAR-Q).
Thomas, S. G., Reading, J., & Shephard, R. J. (1992) · PubMed · Cited by 1,952 · see citing works
Wing, R. R., Lang, W., Wadden, T. A., Safford, M., Knowler, W. C., Bertoni, A. G., Hill, J. O., Brancati, F. L., Peters, A., Wagenknecht, L., et al. (2011) · Diabetes Care · doi:10.2337/dc10-2415 · Cited by 1,828 · cited 1× · see citing works
“…M anuscript Keywords weight loss; self-monitoring; mobile health; adherence Background Obesity remains a major public health concern and behavioral weight loss (BWL) is the gold-standard non-surgical treatment for weight loss (Hales, Fryar, Carroll, Freedman, & Ogden, 2018; Wing et al., 2011).”
BackgroundSelf-citationHales, C. M., Fryar, C. D., Carroll, M. D., Freedman, D. S., & Ogden, C. L. (2018) · JAMA · doi:10.1001/jama.2018.3060 · Cited by 1,761 · see citing works
Self-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 · see citing works
Using Internet technology to deliver a behavioral weight loss program
Tate, D. F. (2001) · JAMA · doi:10.1001/jama.285.9.1172 · Cited by 925 · see citing works
The Look AHEAD Study: A Description of the Lifestyle Intervention and the Evidence Supporting It
The Look AHEAD Research Group (2006) · Obesity · doi:10.1038/oby.2006.84 · Cited by 873 · see citing works
Carter, M. C., Burley, V. J., Nykjaer, C., & Cade, J. E. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2283 · Cited by 666 · see citing works
Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., Rickman, A. D., Wahed, A. S., & Belle, S. H. (2016) · JAMA · doi:10.1001/jama.2016.12858 · Cited by 621 · cited 2× · see citing works
“Given previous studies showing that merely adding tracking tools to BWL may be of no benefit, or even have iatrogenic effects, use of these tools would have to be accompanied by a careful intervention strategy to guide participants in summarizing and interpreting their data to inform behavior change (Jakicic et al., 2…”
BackgroundMethodKrueger, C., & Tian, L. (2004) · Biological Research For Nursing · doi:10.1177/1099800404267682 · Cited by 561 · cited 1× · see citing works
“Missing weights were not imputed because the proposed analyses accommodate multiple missing data points by including a random effect of subject (Edwards, 2000; Krueger & Tian, 2004).”
Payne, H., Lister, C., West, J. H., & Bernhardt, J. M. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3335 · Cited by 525 · see citing works
A qualitative study of user perceptions of mobile health apps
Peng, W., Kanthawala, S., Yuan, S., & Hussain, S. A. (2016) · BMC Public Health · doi:10.1186/s12889-016-3808-0 · Cited by 465 · see citing works
Physical activity and weight loss: does prescribing higher physical activity goals improve outcome?
Jeffery, R. W., Wing, R. R., Sherwood, N. E., & Tate, D. F. (2003) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/78.4.684 · Cited by 412 · see citing works
Self-citationA tutorial on count regression and zero-altered count models for longitudinal substance use data.
Atkins, D. C., Baldwin, S. A., Zheng, C., Gallop, R. J., & Neighbors, C. (2013) · Psychology of Addictive Behaviors · doi:10.1037/a0029508 · Cited by 387 · cited 1× · see citing works
“For frequency of self-monitoring physical activity, a zero-inflated negative binomial distribution with logit link function was used to account for multiple factors contributing to zero values (i.e., no physical activity v. non-adherence to monitoring physical activity; Atkins et al., 2013).”
MethodTurner-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 · cited 1× · see citing works
“One method could be matching individuals to preferred tracking tools or intervention modalities (Turner-McGrievy et al., 2013), and another could be using passive sensing technologies to ease the burden of self-monitoring.”
MethodBehavioral Treatment of Obesity
Wadden, T. A., & Foster, G. D. (2000) · Medical Clinics of North America · doi:10.1016/s0025-7125(05)70230-3 · Cited by 309 · see citing works
Behavioral treatment of obesity
Foster, G. D., Makris, A. P., & Bailer, B. A. (2005) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/82.1.230s · Cited by 297 · see citing works
Using mHealth Technology to Enhance Self-Monitoring for Weight Loss
Burke, L. E., Styn, M. A., Sereika, S. M., Conroy, M. B., Ye, L., Glanz, K., Sevick, M. A., & Ewing, L. J. (2012) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2012.03.016 · Cited by 295 · cited 2× · see citing works
“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).”
MethodResultIntegrating 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 · cited 2× · see citing works
“Electronic self-monitoring tools, as compared to paper-and-pencil tracking, have produced increased adherence to self-monitoring within BWL (Carter, Burley, Nykjaer, & Cade, 2013; Spring et al., 2013; Thomas et al., 2017) and this should presumably lead to greater weight losses (Turk et al.”
MethodA Review of Efficacious Technology-Based Weight-Loss Interventions: Five Key Components
Khaylis, A., Yiaslas, T., Bergstrom, J., & Gore-Felton, C. (2010) · Telemedicine and e-Health · doi:10.1089/tmj.2010.0065 · Cited by 218 · see citing works
Experiences of Self-Monitoring: Successes and Struggles During Treatment for Weight Loss
Burke, L. E., Swigart, V., Warziski Turk, M., Derro, N., & Ewing, L. J. (2009) · Qualitative Health Research · doi:10.1177/1049732309335395 · Cited by 153 · see citing works
Webber, K. H., Tate, D. F., Ward, D. S., & Bowling, J. M. (2010) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2009.03.001 · Cited by 134 · cited 1× · see citing works
“For example, it is likely that adherence behaviors have shared variance and clustering given that they are behavioral representations of similar constructs such as motivation (Webber et al., 2010).”
BackgroundCarels, R. A., Darby, L. A., Rydin, S., Douglass, O. M., Cacciapaglia, H. M., & O'Brien, W. H. (2005) · Annals of Behavioral Medicine · doi:10.1207/s15324796abm3003_2 · Cited by 129 · cited 1× · see citing works
“Moreover, prior research has largely assumed a unidirectional relationship between self-monitoring and weight change, but a full understanding of self-monitoring behavior within BWL may require studying the bidirectional prospective associations (Carels et al., 2005).”
BackgroundBurke, L. E., Styn, M. A., Glanz, K., Ewing, L. J., Elci, O. U., Conroy, M. B., Sereika, S. M., Acharya, S. D., Music, E., Keating, A. L., et al. (2009) · Contemporary Clinical Trials · doi:10.1016/j.cct.2009.07.003 · Cited by 121 · see citing works
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 · cited 6× · see citing works
“Nonetheless, conclusions should be interpreted with caution, as the missing data are likely confounded with an overall pattern of disengagement (Scherer, Ben-Zeev, Li, & Kane, 2017) that could also be related to weight regain observed during the maintenance period (Thomas et al., 2019).”
BackgroundMethodInfluentialSelf-citationWang, J., Sereika, S., Chasens, Ewing, Matthews, & Burke, L. (2012) · Patient Preference and Adherence · doi:10.2147/ppa.s28889 · Cited by 95 · cited 2× · see citing works
“, 2017) and this should presumably lead to greater weight losses (Turk et al., 2013; Wang et al., 2012).”
BackgroundMethodIs self-weighing an effective tool for weight loss: a systematic literature review and meta-analysis
Madigan, C. D., Daley, A., Lewis, A. L., Aveyard, P. N., & Jolly, K. B. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0267-4 · Cited by 91 · see citing works
Health-E-Call, a Smartphone-Assisted Behavioral Obesity Treatment: Pilot Study
Thomas, J. G., & Wing, R. R. (2013) · JMIR mhealth and uhealth · doi:10.2196/mhealth.2164 · Cited by 69 · see citing works
Self-citationSelf-Monitoring as a Mediator of Weight Loss in the SMART Randomized Clinical Trial
Turk, M. W., Elci, O. U., Wang, J., Sereika, S. M., Ewing, L. J., Acharya, S. D., Glanz, K., & Burke, L. E. (2013) · International Journal of Behavioral Medicine · doi:10.1007/s12529-012-9259-9 · Cited by 66 · cited 2× · see citing works
“, 2017) and this should presumably lead to greater weight losses (Turk et al., 2013; Wang et al., 2012).”
BackgroundMethodAnalyzing mHealth Engagement: Joint Models for Intensively Collected User Engagement Data
Scherer, E. A., Ben‐Zeev, D., Li, Z., & Kane, J. M. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6474 · Cited by 61 · see citing works
Behavioral Treatment of the Patient with Obesity
Alamuddin, N., & Wadden, T. A. (2016) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2016.04.008 · Cited by 59 · cited 1× · see citing works
“In addition to daily selfmonitoring of diet, physical activity, and weight, participants were taught behavioral skills such as stimulus control, meal planning, and problem solving to facilitate adherence and to address common barriers to weight loss (Alamuddin & Wadden, 2016).”
BackgroundBorg, P. T., Fogelholm, M., & Kukkonen-Harjula, K. (2004) · International Journal of Obesity · doi:10.1038/sj.ijo.0802790 · Cited by 58 · see citing works
The role of self-monitoring in the maintenance of weight loss success
Laitner, M. H., Minski, S. A., & Perri, M. G. (2016) · Eating Behaviors · doi:10.1016/j.eatbeh.2016.03.005 · Cited by 49 · see citing works
Assessing dietary intake in the management of obesity
O'Neil, P. M. (2001) · Obesity Research · doi:10.1002/oby.2001.9.s5.361 · Cited by 48 · cited 1× · see citing works
“Therefore, tools and techniques that facilitate improved self-monitoring of dietary intake, weight, and physical activity are necessary to improve weight loss and weight maintenance outcomes (Borg, Fogelholm, & Kukkonen-Harjula, 2004; Burke, Styn, et al., 2009; O’Neil, 2001).”
Thomas, J. G., Raynor, H. A., Bond, D. S., Luke, A. K., Cardoso, C. C., Wojtanowski, A. C., Vander Veur, S., Tate, D., Wing, R. R., & Foster, G. D. (2017) · Obesity Science & Practice · doi:10.1002/osp4.132 · Cited by 45 · cited 4× · see citing works
“Electronic self-monitoring tools, as compared to paper-and-pencil tracking, have produced increased adherence to self-monitoring within BWL (Carter, Burley, Nykjaer, & Cade, 2013; Spring et al., 2013; Thomas et al., 2017) and this should presumably lead to greater weight losses (Turk et al.”
BackgroundMethodInfluentialSelf-citationTanenbaum, M. L., Ross, K. M., & Wing, R. R. (2016) · Obesity · doi:10.1002/oby.21650 · Cited by 20 · cited 1× · see citing works
“This may have led participants to avoid self-monitoring, which might remind them of poor outcomes and associated unpleasant emotions (Tanenbaum et al., 2016).”
BackgroundSelf-citationRiley, W. T. (2017) · Health Psychology · doi:10.1037/hea0000464 · Cited by 12 · cited 2× · see citing works
“, meaningful over shorter time scales like one month) when examined over an entire long-term BWL (Riley, 2017).”
BackgroundSmall weight gains during obesity treatment: normative or cause for concern?
Schumacher, L. M., Gaspar, M., Remmert, J. E., Zhang, F., Forman, E. M., & Butryn, M. L. (2016) · Obesity Science & Practice · doi:10.1002/osp4.73 · Cited by 3 · cited 1× · see citing works
“Although the current finding requires replication, it suggests that interventionists delivering in-person, group-based treatments should be aware that minor (month-to-month) weight loss failures can have problematic consequences (Schumacher et al., 2016).”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Scherrenberg, M., Wilhelm, M., Hansen, D., Völler, H., Cornelissen, V., Frederix, I., Kemps, H., & Dendale, P. (2021) · European Journal of Preventive Cardiology · doi:10.1177/2047487320939671 · Cited by 251 · see citing works
“Goldstein et al.(84) and Muralidharan et al.”
Patel, M. L., Wakayama, L. N., & Bennett, G. G. (2021) · Obesity · doi:10.1002/oby.23088 · Cited by 206 · see citing works
“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).”
BackgroundJehan, S., Zizi, F., Pandi-Perumal, S. R., McFarlane, S. I., Jean‐Louis, G., & Myers, A. K. (2020) · Advances in Obesity Weight Management & Control · doi:10.15406/aowmc.2020.10.00321 · Cited by 57 · see citing works
Raber, M. R., Liao, Y., Rara, A., Schembre, S. M., Krause, K. J., Strong, L., Daniel-MacDougall, C., & Basen-Engquist, K. (2021) · Public Health Nutrition · doi:10.1017/s136898002100358x · Cited by 47 · see citing works
“) number of diet entries submitted 41·2% | Mean (SD)= 38 (33) Daily diet entry website feature usage was associated with weight change at 3 months (r= 0·71; P< 0·001) and 6 months (r= 0·72; P< 0·001)”
BackgroundMetzendorf, M., Wieland, L. S., & Richter, B. (2024) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013591.pub2 · Cited by 48 · see citing works
Krukowski, R. A., Denton, A. H., & König, L. M. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-023-01555-6 · Cited by 38 · see citing works
“Self-monitoring of dietary intake, physical activity, and weight plays a key role in these programs [3, 4], and greater adherence to self-monitoring has been demonstrated to be the best predictor of weight loss success [5–8].”
BackgroundEisenhauer, C. M., Brito, F., Kupzyk, K. A., Yoder, A. M., Almeida, F. A., Johnson-Beller, R., Miller, J. J., & Hageman, P. A. (2021) · BMC Public Health · doi:10.1186/s12889-021-11618-7 · Cited by 36 · see citing works
“Self-monitoring of physical activity, weight, and dietary intake is positively associated with weight loss [9].”
BackgroundExercise in Obesity—the Role of Technology in Health Services: Can This Approach Work?
Dobbie, L. J., Tahrani, A. A., Alam, U., James, J., Wilding, J. P. H., & Cuthbertson, D. J. (2021) · Current Obesity Reports · doi:10.1007/s13679-021-00461-x · Cited by 26 · see citing works
Adherence 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 24 · see citing works
Tronieri, J. S., Wadden, T. A., Walsh, O., Berkowitz, R. I., Alamuddin, N., & Chao, A. M. (2020) · Behaviour Research and Therapy · doi:10.1016/j.brat.2020.103639 · Cited by 21 · see citing works
“Although we discuss adherence behaviors as predictors of weight loss, weight loss also may influence adherence, such as by improving motivation to continue complying with components of the intervention (Goldstein et al., 2019).”
BackgroundCarpenter, C. A., Eastman, A., & Ross, K. M. (2022) · JMIR Formative Research · doi:10.2196/33603 · Cited by 22 · see citing works
“Finally, this study did not investigate week-to-week associations between changes in weight, dietary intake, and physical activity and later changes in self-monitoring behaviors; however, some emerging research suggests that there may be important bidirectional associations between self-monitoring and changes in weigh…”
BackgroundEffectiveness of a self‐regulation intervention for weight loss: A randomized controlled trial
Frie, K., Hartmann‐Boyce, J., Jebb, S. A., & Aveyard, P. N. (2020) · British Journal of Health Psychology · doi:10.1111/bjhp.12436 · Cited by 22 · see citing works
“We found that adherence to selfweighingwas a significant predictor ofweight loss success in the control arm too, and this matches other observational data (Goldstein et al., 2019; LaRose, Lanoye, Tate, & Wing, 2016; Van Wormer, French, Pereira, & Welsh, 2008).”
ResultCrane, N. T., Hagerman, C. J., Horgan, O. Z., & Butryn, M. L. (2023) · JMIR mhealth and uhealth · doi:10.2196/45057 · Cited by 17 · see citing works
“Past studies show that people are less likely to weigh themselves when they have recently gained weight [50] or eaten more calories than usual [24], suggesting that the declining adherence for self-weighing may be a result of avoidance of the scale as eating and exercise behavior become less stringent than they were a…”
BackgroundKrukowski, R. A., Harvey, J. R., Borden, J., Stansbury, M. L., & West, D. S. (2021) · Obesity Science & Practice · doi:10.1002/osp4.586 · Cited by 14 · see citing works
“1%); and (6) monitor only body weight (65.”
BackgroundTouloudi, E., Hassandra, M., Galanis, E., Pinnas, G., Κrommidas, C., Γούδας, Μ., & Theodorakis, Y. (2025) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2025.1520068 · Cited by 16 · see citing works
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 · see citing works
“Others have defined adherence as percent of days with recording [20, 21].”
Engaging primary care patients with existing online tools for weight loss: A pilot trial
McVay, M. A., Cooper, K. B., Donahue, M. L., Seoane, M. C., Shah, N. R., Webb, F. J., Perri, M. G., & Jake‐Schoffman, D. E. (2022) · Obesity Science & Practice · doi:10.1002/osp4.592 · Cited by 12 · see citing works
Ratnapalan, M., Coghlan, B., Tan, M., Everitt, H., Geraghty, A. W. A., Little, P. S., Lewith, G. T., & Bishop, F. L. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-032524 · Cited by 12 · see citing works
Self-Monitoring of Weight as a Weight Loss Strategy: A Systematic Review
Hallock, R., Ufholz, K. E., & Patel, N. (2024) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-024-00746-5 · Cited by 11 · see citing works
“Additionally, studies have consistently supported electronic self-monitoring tools when compared to pen to paper log tracking, result in increased adherence to self-monitoring in behavioral weight-loss strategies [7, 16–18].”
The limits of inconspicuous incentives
John, L. K., Blunden, H., Milkman, K. L., Foschini, L., & Tuckfield, B. (2022) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2022.104180 · Cited by 11 · see citing works
Goh, C., Zheng, K., Wen, Y.-C., Nguyen, T., Liu, C.-S., Koh, C. K., Lee, G. K. Y., Tay, C., Ooi, B., & Wong, M. (2024) · Digital Health · doi:10.1177/20552076241228433 · Cited by 10 · see citing works
Gen Z’s enduring commitment to fitness apps: a comprehensive examination
Sharma, V., Mahajan, V., Kapse, M., Karhale, A. H., & Surwade, S. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2419483 · Cited by 9 · see citing works
“Their consistent engagement with app features, such as goal setting and progress visualization contributes significantly to sustained adherence and goal attainment (Goldstein et al., 2019).”
Goldstein, S. P., Thomas, J. G., Brick, L. A. D., Zhang, F., & Forman, E. M. (2021) · Appetite · doi:10.1016/j.appet.2021.105440 · Cited by 8 · see citing works
Self-citationLim, W. X., Fook-Chong, S. M. C., Lim, J. W., & Gan, W. H. (2022) · JMIR Nursing · doi:10.2196/36811 · Cited by 6 · see citing works
Saarikko, J., Axelin, A., Huvinen, E., Rahmani, A. M., Kolari, T. A., & Niela‐Vilén, H. (2024) · Midwifery · doi:10.1016/j.midw.2024.104235 · Cited by 7 · see citing works
Blow, J. A., Sagaribay, R., & Cooper, T. V. (2022) · Appetite · doi:10.1016/j.appet.2022.105979 · Cited by 7 · see citing works
Sharma, V., Payal, R., Dutta, K., Poulose, J., & Kapse, M. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2391124 · Cited by 10 · see citing works
“Previous research highlights that self-monitoring of fitness-related activities is a core attribute offered by fitness apps (ahn et al., 2020; goldstein et al., 2019; hu et al., 2015, 2020). this self-monitoring aspect is critical as it empowers users to take control of their fitness goals by…”
Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment
Hagerman, C. J., Onu, M. C., Crane, N. T., Butryn, M. L., & Forman, E. M. (2024) · Journal of Behavioral Medicine · doi:10.1007/s10865-024-00476-4 · Cited by 7 · see citing works
“Past studies consistently show a pattern in which individuals disengage from their weight control goals after gaining weight (Frie et al., 2020a ; Goldstein et al., 2019; Hayes et al., 2022 ).”
BackgroundInfluentialWeaver, R. G., Armstrong, B., Adams, E. L., Beets, M. W., White, J., Flory, K., Wilson, D. K., McLain, A. C., & Tennie, B. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01312-3 · Cited by 5 · see citing works
“Parent intervention The PI was founded on goal setting [29, 30] and behavioral monitoring of their child’s sleep and physical activity [31], which are key components that underly effective behavioral interventions [32].”
BackgroundAssessing Interventional Components in a Weight Loss App
Agyei, E. E. Y., Oinas-Kukkonen, H., Nyman, V., Virkkula, T., Oikarinen, N., Merikallio, H., Savolainen, M., & Hukkanen, J. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.363 · Cited by 5 · see citing works
“Prior studies have noted the relevance of educational content (Vlahu-Gjorgievska et al., 2018), and self-Page monitoring tools, among other features, as important for weight management apps (Burke et al., 2011; Goldstein et al., 2019; Vlahu-Gjorgievska et al., 2018).”
Sundgot‐Borgen, J., Baardstu, S., Bond, D. S., Sniehotta, F. F., Bergh, I., Mala, T., Rø, Ø., & Kvalem, I. L. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01739-2 · Cited by 4 · see citing works
Self-citationHilbert, A., Juarascio, A. S., Prettin, C., Petroff, D., Schlögl, H., & Hübner, C. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-064394 · Cited by 4 · see citing works
The Behavioral Treatment of Obesity
Chao, A. M., Quigley, K. M., & Wadden, T. A. (2022) · Wiley eBooks · doi:10.1002/9781119695257.ch19 · Cited by 4 · see citing works
Wang, D., Rojo‐Tirado, M. Á., Benito, P. J., Rubio‐Arias, J. Á., Ramos‐Campo, D. J., & Marques, M. M. (2025) · Obesity Reviews · doi:10.1111/obr.70066 · Cited by 3 · see citing works
“Goldstein et al. [31] Adherence to medication Adherence to medication is defined as the extent to which patients follow their healthcare providers' recommendations, and for patients with chronic diseases, it is linked with disease control, hospital admission rate, morbidity, and even mortality.”
InfluentialBehaviors and psychological states associated with transitions from regaining to losing weight.
Hayes, J. F., Wing, R. R., Unick, J. L., & Ross, K. M. (2022) · Health Psychology · doi:10.1037/hea0001224 · Cited by 3 · see citing works
Self-citationExploring the benefits and costs of a growth mindset in a digital app weight management program
Earl, S., Burnette, J. L., & Ho, A. S. (2024) · Journal of Health Psychology · doi:10.1177/13591053241226610 · Cited by 3 · see citing works
“General engagement, such as completing modules, weigh-ins, and engaging in coaching conversations also predicted weight loss (Auster-Gussman et al., 2022; Goldstein et al., 2019).”
Balakrishnan, P., Owen, E. P., Eberl, M., Friedrich, B., & ETTER, T. J. (2022) · Cardiovascular Endocrinology & Metabolism · doi:10.1097/xce.0000000000000266 · Cited by 3 · see citing works
Medical Weight Management: A Multidisciplinary Approach
Elhag, W., & Ansari, W. E. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.105475 · Cited by 3 · see citing works
Feasibility of Telehealth and Innovative Technologies to Limit Excessive Gestational Weight Gain
Mattson, R., & Barger, M. K. (2023) · Nursing for Women s Health · doi:10.1016/j.nwh.2023.08.002 · Cited by 3 · see citing works
Remote Delivery of Partial Meal Replacement for Weight Loss in People Awaiting Arthroplasty
Chimoriya, R., Naylor, J., Mitlehner, K., Adie, S., Harris, I. A., Bell-Higgs, A., Brosnahan, N., & Piya, M. K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13113227 · Cited by 2 · see citing works
“…remotely during COVID-19 restrictions, this study’s findings indicate that the PMR weight loss program was feasible and well accepted by people awaiting TKA or THA, with many participants reporting willingness to pay for the service if offered on a long-term basis following the arthroplasty.”
BackgroundThe Effects of Implicit Theories on Body Weight Information Avoidance
Hagerman, C. J., Stock, M. L., Post, S. M., Macura, Z., Moore, P. J., Dodge, T. L., & Wirtz, P. W. (2023) · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · doi:10.1027/1618-3169/a000585 · Cited by 2 · see citing works
Holly, S., Caruso, O. T., Smaranda, D., McEachern, L. W., Seabrook, J. A., & Gilliland, J. (2025) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.70130 · Cited by 1 · see citing works
Krukowski, R. A., Paulikonis, K., Markwalter, T. H., Minor, S., & Ross, K. M. (2025) · Obesity Science & Practice · doi:10.1002/osp4.70104 · Cited by 1 · see citing works
de Oliveira, R. S. C., Nabwire, S., Rantaiso, S., Merikallio, H., Savolainen, M. J., Hukkanen, J., & B, H. O.-K. A. (2025) · Digital Health · doi:10.1177/20552076251395530 · Cited by 1 · see citing works
“9,10 Integrating it into mHBCSS is a key approach for enhancing user engagement with health behaviors and digital interventions, thereby promoting favorable outcomes.”
A Novel Web App for Dietary Weight Management: Development, Implementation, and Usability Study
Oliveira, A., Wolff, J., Alfouzan, N. W., Yu, J. Z., Yahya, A., Lammy, K., & Nakamura, M. (2024) · JMIR Formative Research · doi:10.2196/58363 · Cited by 1 · see citing works
Nezami, B. T., Valle, C. G., Brinson, A., Espeland, M. A., Wing, R. R., & Tate, D. F. (2021) · Obesity · doi:10.1002/oby.23123 · Cited by 1 · see citing works
“A recent analysis found that greater adherence to paper self- monitoring of diet, weight, and activity was associated with concurrent weight change, and that lower weight losses were associated with lower self- monitoring in the following month (23), but few studies have evaluated day- to- day objective predictors of…”
BackgroundSelf-citationBerry, M. P., Seburg, E. M., Butryn, M. L., Jeffery, R. W., Crane, M. M., Levy, R. L., Forman, E. M., & Sherwood, N. E. (2021) · Translational Behavioral Medicine · doi:10.1093/tbm/ibab011 · Cited by 1 · see citing works
“This instruction was made based on an assumption that a lack of self-monitoring likely also reflects poor adherence to dietary guidelines, given robust associations between self-monitoring adherence and weight outcomes [16,24,25].”
BackgroundTreating Bulimia Nervosa and Achieving Medically Required Weight Loss: A Case Study
McDonald, J. B., & Rancourt, D. (2021) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2021.06.008 · Cited by 1 · see citing works
Poglodzinski, J., Deschamps, B. A., McCarthy, M. S., Cole, R. E., Elshaw, E. B., & Szekely, B. M. (2021) · doi:10.2196/preprints.35314
Boutelle, K. N., Sim, D. E. K., Eichen, D. M., Manzano, M. A., Rhee, K. E., & Strong, D. R. (2024) · International Journal of Obesity · doi:10.1038/s41366-024-01574-8 · Cited by 1 · see citing works
Goldstein, C. M., Goldstein, S. P., Ladd, B. T., Wing, R. R., & Thomas, J. G. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00448-0
“often follows weight loss typically coincides with reduced self-monitoring frequency (Goldstein et al., 2019; Loveman et al., 2011).”
Self-citationCheatham, S. W., Sutton, B. G., Ambler-Wright, T., Cheatham, C. J., & Ludwig, C. M. (2025) · International Journal of Sports Physical Therapy · doi:10.26603/001c.132493 · Cited by 1 · see citing works
Hedin, L., Hagströmer, M., Marcus, C., & Danielsson, P. (2025) · Sage Open Pediatrics · doi:10.1177/30502225251348292
Weight loss following nutritional education session among newly diagnosed breast cancer patients
Sauls, R., Rozen, E., Doerr, V., Brown, N. I., Buro, A. W., Riccardi, D., Mallory, M. A., Laronga, C., Hoover, S. J., Pabbathi, S. R., et al. (2025) · Supportive Care in Cancer · doi:10.1007/s00520-025-10142-3
“Another study reported similar results, where weight concern at the start of the study improved self-awareness and adherence to a weight loss intervention, which improved weight loss [33].”
Understanding the needs and preferences of oncology dietitians for mobile health apps
Solano, O. R., Morris, G., Stump, T. K., Colvin, C. A., Tan, G., Chang, J., & Lin, A. (2025) · Nutrition · doi:10.1016/j.nut.2025.113069
Tobin, S. Y., Sherman, S. I., Bock, B. C., DUNSIGER, S. I., Thomas, J. G., Goldstein, S. P., & Unick, J. L. (2026) · JMIR Research Protocols · doi:10.2196/78973
“Participants will be instructed to self-monitor body weight, energy intake, and PA daily over the 12-month WL period, as self-monitoring in a behavioral WL program has been shown to be associated with weight change [56].”
Self-citationPower, J. M., Hurley, L., Gottfredson, N. C., Nezami, B. T., Sciamanna, C., & Tate, D. F. (2026) · Digital Health · doi:10.1177/20552076261434062
Hayes, J. F., LaRose, J. G., Derrick, A., Dugas, N., Fu, E., Elwy, A. R., & Wing, R. R. (2026) · Translational Behavioral Medicine · doi:10.1093/tbm/ibag044
Laurent, N. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1879746
Nabwire, S., de Oliveira, R. S. C., Merikallio, H., Savolainen, M. J., Hukkanen, J., & B, H. O.-K. A. (2026) · Health Informatics Journal · doi:10.1177/14604582261480535
Self-Monitoring of Weight Loss Over Time: Secondary Data Analysis of a Randomized Controlled Trial
de Oliveira, R. S. C., Nabwire, S., Merikallio, H., Savolainen, M. J., Hukkanen, J., & B, H. O.-K. A. (2026) · JMIR mhealth and uhealth · doi:10.2196/71604
- Authors
- Goldstein, S. P., Goldstein, C. M., Bond, D. S., Raynor, H. A., Wing, R. R., & Thomas, J. G.
- Affiliations
- Brown University (US)
- University of Tennessee at Knoxville (US)
- ORCID
- Venue
- Health Psychology
- Published
- 2019-12
- Volume
- 38
- Issue
- 12
- Pages
- 1128-1136
- ISSN
- 0278-6133 (ISSN-L)
- 1930-7810
- PMC
- PMC6861632
- OpenAlex
- W2976554951
- Cited by
- 63
- References
- 39
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 14 · 2023: 6 · 2022: 9 · 2021: 12 · 2020: 5
- FWCI
- 8.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Health Psychology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK095779)
- 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
- self-monitoring, weight change, behavioral weight loss, obesity, overweight, mobile application, group intervention, dietary monitoring, self-weighing, physical activity monitoring, medication adherence, social influence
- MeSH terms
- Smartphone, Behavior Therapy, Female, Humans, Male, Middle Aged, Weight Loss, Exercise
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