Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes. Existing research examining lapse risk factors has had methodological limitations, including retrospective recall biases, subjective operationalizations of lapse, and has investigated lapses among participants in gold-standard behavioral weight loss programs (which are not accessible to most Americans). The current study will address these limitations by being the first to prospectively assess several risk factors of lapse (objectively operationalized) in the context of a commercial mobile health (mHealth) intervention, a highly popular and accessible method of weight loss. N = 159 adults with overweight or obesity enrolled in an mHealth commercial weight loss program completed ecological momentary assessments (EMAs) of 15 risk factors and lapses (defined as exceeding a point target for a meal/snack) over a 2-week period. N = 9 participants were excluded due to low EMA compliance, resulting in a sample of N = 150. Dietary lapses were predicted by momentary increases in urges to deviate from one's eating plan (b = .55, p < .001), cravings (b = .55, p < .001), alcohol consumption (b = .51, p < .001), and tiredness (b = .19, p < .001), and decreases in confidence related to meeting dietary goals (b = -.21, p < .001) and planning food intake (b = -.15, p < .001). This study was among the first to identify prospective predictors of lapse in the context of a commercial mHealth weight loss program. Findings can inform mHealth weight loss programs, including just-in-time interventions that measure these risk factors, calculate when risk of lapse is high, and deliver momentary interventions to prevent lapses.
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Source |
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http://dx.doi.org/10.1007/s10865-021-00264-4 | DOI Listing |
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