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Online participant recruitment ("crowdsourcing") platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low-quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower-quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs.

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Verbal data provide researchers insight beyond that offered by text-based responses, including tone, reasoning elaboration, and experienced difficulty, among other processes. Additionally, it offers a less cognitively taxing way for participants to provide long responses. Verbal data collection methods are found in a variety of fields, mostly conducted in lab-based settings or requiring specialized hardware. Restricting verbal protocols to lab-based settings can have several drawbacks, including smaller sample sizes, biased populations, reduced adoption, and incompatibility with potential social distancing requirements. No method currently exists for researchers to collect verbal data within major online survey collection platforms. The current paper offers a user-friendly approach for collecting verbal data online, where a researcher can copy and paste JavaScript code into the desired survey platform. By providing a framework that does not require any advanced programming ability, researchers can collect verbal data in a scalable way using familiar modalities.

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Introduction: Preliminary studies suggest that infection with Bartonella bacteria can not only cause a characteristic rash, headache, fever, and fatigue but also neuropsychiatric symptoms. To date, this association has only been reported in case studies, and it remains unclear if this association generalizes to larger samples. Methods: We used Amazon's Mechanical Turk (MTurk) to crowdsource a large sample (N = 996) of individuals to ascertain the extent to which the presence of participant-identified Bartonella-associated cutaneous lesions (BACL) was associated with self-reported measures of anxiety, depression, and schizotypy. Participants were asked to select images of cutaneous lesions they had seen on their own bodies and complete a battery of self-report questionnaires to assess psychiatric symptoms. Participants were not informed that the focus of the study was on potential dermatological lesions associated with Bartonella. Point-biserial correlations were used to determine the potential relationship between selecting a BACL image and the severity of self-reported psychiatric symptoms. Results: Scores of anxiety, depression, and schizotypy were positively and significantly correlated with selecting a BACL image. Furthermore, self-report scores of 10 or higher on the GAD-7 and PHQ-9, which represent the suggested clinical cutoffs for meeting criteria for a depressive or anxiety-related disorder, were also significantly associated with selecting a BACL image. Non-Bartonella-associated cutaneous legions were also significantly associated with self-reported measures of psychiatric symptoms. Discussion: The current study broadens the link between the presence of BACL and the presence of psychiatric symptoms of anxiety, depression, and schizotypy and extends a potential relationship beyond the small sample sizes of previous case studies and case series. Further investigation is recommended to address limitations and expand on these findings.

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Objective: Currently available contraceptive methods do not meet the needs of all users. We sought to explore preferences of potential end-users regarding an on-demand, non-hormonal female contraceptive currently under development, using a web-based survey. Study design: We recruited respondents for an exploratory survey via web link on Amazon Mechanical Turk (MTurk). Individuals were eligible if they were 18-44 years of age, identified as cis-gender female, were English-speaking, not pregnant, and had used barrier contraception previously. Respondents provided demographic characteristics and a basic reproductive history. We then provided a brief description of the potential contraceptive. Respondents were asked about their interest in the proposed contraceptive and preferences for method attributes. Results: A total of 500 respondents completed the survey. Three-quarters of respondents were <35 years of age and 48.2% were currently using a barrier contraceptive method. Three-fourths of respondents (73.8%) expressed interest in using the contraceptive under development. The majority wanted the method to be small (≤2 inches), rod-shaped, and low cost (<$5 per use). More than half (59.4%) said it was important to be able to use the method without partners' knowledge. The most reported potential concerns were vaginal irritation (51.6%) and lack of effectiveness (46.4%). Sixty percent of respondents were confident they could use the method correctly. Discussion: Available contraceptive methods lack attributes preferred by some users. Development of new contraceptives frequently does not involve end-user input early in the development process. Individuals in this sample displayed interest in the proposed contraceptive and expressed preferences that can inform the further development of this method.

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BACKGROUND CONTEXT: With rising healthcare expenditures in the United States, patients and providers are searching to maintain quality while reducing costs. PURPOSE: The aim of this study was to investigate patient willingness to pay for anterior cervical discectomy and fusion (ACDF), degenerative lumbar spinal fusions (LF), and adult spine deformity (ASD) surgery. STUDY DESIGN/SETTING: A survey was developed and distributed to anonymous respondents through Amazon Mechanical Turk (MTurk). METHODS: The survey introduced 3 procedures: ACDF, LF, and ASD surgery. Respondents were asked sequentially if they would pay at each increasing price option. Respondents were then presented with various cost-saving methods and asked to select the options that made them most uncomfortable, even if those would save them out-of-pocket costs. RESULTS: In total, 979 of 1,172 total responses (84%) were retained for analysis. The average age was 36.2 years and 44% of participants reported a household income of $50,000 to 100,000. A total of 63% used Medicare and 13% used Medicaid. A total of 40% stated they had high levels of financial stress. A total of 30.1% of participants were willing to undergo an ACDF, 30.3% were willing to undergo a LF, and 29.6% were willing to undergo ASD surgery for the cost of $3,000 (p=.98). Regression demonstrated that for ACDF surgery, a $100 increase in price resulted in a 2.1% decrease in willingness to pay. This is comparable to degenerative LF surgery (1.8% decrease), and ASD surgery (2%). When asked which cost-saving measures participants were least comfortable with for ACDF surgery, 60% stated "Use of the older generation implants/devices" (LF: 51%, ASD: 60%,), 61% stated "Having the surgery performed at a community hospital instead of at a major academic center" (LF: 49%, ASD: 56%), and 55% stated "Administration of anesthesia by a nurse anesthetist" (LF: 48.01%, ASD: 55%). Conversely, 36% of ACDF patients were uncomfortable with a "Video/telephone postoperative visit" to cut costs (LF: 51%, ASD: 39%). CONCLUSIONS: Patients are unwilling to contribute larger copays for adult spinal deformity correction than for ACDF and degenerative lumbar spine surgery, despite significantly higher procedural costs and case complexity/invasiveness. Patients were most uncomfortable forfeiting newer generation implants, receiving the operation at a community rather than an academic center, and receiving care by physician extenders. Conversely, patients were more willing to convert postoperative visits to telehealth and forgo neuromonitoring, indicating a potentially poor understanding of which cost-saving measures may be implemented without increasing the risk of complications.

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Although Amazon Mechanical Turk facilitates the quick surveying of a large sample from various demographic and socioeconomic backgrounds, it may not be an optimal platform for obtaining reliable diabetes-related information from the online type 1 diabetes population.

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BACKGROUND: Researchers have implemented multiple approaches to increase data quality from existing web-based panels such as Amazon's Mechanical Turk (MTurk). OBJECTIVE: This study extends prior work by examining improvements in data quality and effects on mean estimates of health status by excluding respondents who endorse 1 or both of 2 fake health conditions ("Syndomitis" and "Chekalism"). METHODS: Survey data were collected in 2021 at baseline and 3 months later from MTurk study participants, aged 18 years or older, with an internet protocol address in the United States, and who had completed a minimum of 500 previous MTurk "human intelligence tasks." We included questions about demographic characteristics, health conditions (including the 2 fake conditions), and the Patient Reported Outcomes Measurement Information System (PROMIS)-29+2 (version 2.1) preference-based score survey. The 3-month follow-up survey was only administered to those who reported having back pain and did not endorse a fake condition at baseline. RESULTS: In total, 15% (996/6832) of the sample endorsed at least 1 of the 2 fake conditions at baseline. Those who endorsed a fake condition at baseline were more likely to identify as male, non-White, younger, report more health conditions, and take longer to complete the survey than those who did not endorse a fake condition. They also had substantially lower internal consistency reliability on the PROMIS-29+2 scales than those who did not endorse a fake condition: physical function (0.69 vs 0.89), pain interference (0.80 vs 0.94), fatigue (0.80 vs 0.92), depression (0.78 vs 0.92), anxiety (0.78 vs 0.90), sleep disturbance (-0.27 vs 0.84), ability to participate in social roles and activities (0.77 vs 0.92), and cognitive function (0.65 vs 0.77). The lack of reliability of the sleep disturbance scale for those endorsing a fake condition was because it includes both positively and negatively worded items. Those who reported a fake condition reported significantly worse self-reported health scores (except for sleep disturbance) than those who did not endorse a fake condition. Excluding those who endorsed a fake condition improved the overall mean PROMIS-29+2 (version 2.1) T-scores by 1-2 points and the PROMIS preference-based score by 0.04. Although they did not endorse a fake condition at baseline, 6% (n=59) of them endorsed at least 1 of them on the 3-month survey and they had lower PROMIS-29+2 score internal consistency reliability and worse mean scores on the 3-month survey than those who did not report having a fake condition. Based on these results, we estimate that 25% (1708/6832) of the MTurk respondents provided careless or dishonest responses. CONCLUSIONS: This study provides evidence that asking about fake health conditions can help to screen out respondents who may be dishonest or careless. We recommend this approach be used routinely in samples of members of MTurk.

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BACKGROUND: Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics-based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives. OBJECTIVE: The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting. METHODS: We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants' baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants' willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality. RESULTS: A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants' willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions. CONCLUSIONS: In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.

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With an escalating popularity of online surveys in behavioral research, it is critical to understand how different sources of participants can yield differing outcomes. While Mturk has been used for online surveys for almost two decades, a recent introduction of online panels allows researchers to choose participants from variety of pools. This study aims to contribute to existing knowledge of how participants from different online platforms differ in their characteristics and behavioral responses which might affect the outcomes. 300 participants were recruited each from Mturk and Prime panels for a 20 mins long survey assessing perceptions and intentions to use Heated tobacco products (HTPs). Participants answered demographic and tobacco-use related questions including their vaccination and masking for COVID-19. They were shown a picture and description of a recently launched HTP. Further, participants answered questions about their awareness of HTPs, risk perception of health conditions from use of different tobacco products (cigarettes, e-cigarettes and HTPs) and perceived severity of COVID-19 infection in smokers, vapers and HTP users. Results showed significant differences in Mturk and Prime panel participants' demographics and tobacco-use. Prime panels showed more racially diverse population (chi-sq = 10.07, p < 0.02) and significantly more current smokers (chi-sq = 44.74, p < 0.01) and current e-cigarette users (chi-sq = 38.04, p < 0.01) compared with Mturk. Mean perception scores for COVID risk in tobacco users were significantly different between Prime panels and Mturk. Study highlights significant differences in sample composition and responses that might be helpful in choosing one online platform over another based on specific study requirements.

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The present study compared the two most prominent procedures for measuring delay discounting, the Adjusting-Amount procedure and the Monetary Choice Questionnaire (MCQ). Of interest was whether the two procedures measure the same construct. Results obtained from two online samples recruited using the Prolific (N = 150) and MTurk (N = 243) platforms revealed generally similar results for both procedures. Regardless of the procedure, the sample, the reward amount, and whether the discounting measure used was theoretically based (i.e., log k) or was atheoretical (i.e., area under the curve, proportion of choices of the delayed reward option), the degree of discounting on the Adjusting-Amount procedure was highly correlated with the degree of discounting on the MCQ, consistent with the hypothesis that both procedures assess the same construct.

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Even before the COVID-19 pandemic, the popularity of online behavioral experiments grew steadily. Due to lockdowns, online studies often became the only available option for behavioral economists, sociologists, and political scientists. The use of the most well-known platforms, such as mTurk, was so intensive that the quality of data was harmed. However, even before the pandemic-induced quality crisis, online studies were limited in scope; real-time interactions between participants were hard to achieve due to the large proportion of drop-outs and issues with creating stable groups. Using the relatively unknown crowdsourcing platform, Toloka, we successfully ran several multi-round interactive experiments. Toloka's sizeable online population, fairly low exposure of participants to sociological surveys and behavioral studies, and convenient application programming interface can make it a useful addition to the toolbox of an experimentalist who needs to run behavioral studies that require real-time interactions between participants.

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OBJECTIVES: This study aimed to examine (1) the association between patient activation (PA), health locus of control (HLOC), sociodemographic and clinical factors, and (2) the effect of HLOC dimensions, sociodemographic and clinical factors on PA. METHODS: Three hundred U.S. adults, with at least one chronic condition (CC) were recruited through Amazon Mechanical Turk and completed an online survey which included sociodemographic questions, the Patient Activation Measure® - 10, and the Multidimensional Locus of Control (MHLC) - Form B. Statistical analyses, including descriptive, correlation, and multiple linear regression, were conducted using IBM SPSS v25. RESULTS: Of the 300 participants, more than half were male (66.3%), White (70.7%), with at least a college degree (76.0%), and employed full-time (79.0%). The average PA score was 68.8 ± 14.5. Multiple linear regression indicated that participants who reported they were Black, retired, with a greater number of CCs, and with higher scores in Chance MHLC had higher PA, while participants with higher scores in Internal MHLC, were unemployed and reported to have been affected by COVID-19-related worry or fear to manage their CC, had lower PA. DISCUSSION: HLOC dimensions should be addressed concurrently with PA for patients with CCs, thus adding to a more patient-centered clinical approach.

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Background: Recent literature on autologous breast reconstruction suggests that such factors as scar symmetry and skin paddle size impact patient preferences more than preservation of native breast skin. Since patient satisfaction with plastic surgery procedures can be largely influenced by beauty standards set by the general public, this study used a novel crowdsourcing method to evaluate laypeople's aesthetic preferences for different bilateral autologous breast reconstructions to determine the relative importance of scar and skin paddle symmetry and preservation of native skin. Methods: Using Amazon's Mechanical Turk crowdsourcing marketplace, participants ranked images of reconstructions based on overall aesthetic appearance. Images were digitally modified to reflect 4 types of reconstruction: immediate (IR), delayed symmetric (DS), delayed asymmetric (DA), or mixed (MR). Results: DS was ranked most favorably (1.74), followed by IR (1.95), DA (2.93), and MR (3.34). Friedman rank sum and pairwise tests showed statistical significance for comparisons of all 4 reconstruction types. Likert ratings were higher for IR than for DA reconstructions for skin quality (P = .002), scar visibility (P < .001), scar position (P < .001), and breast symmetry, shape, and position (P < .001). Ratings increased for all aesthetic factors following nipple-areolar-complex reconstruction (P < .001). Conclusions: More symmetric breast scars were rated aesthetically higher than nonsymmetric scarring, and our participants preferred maintenance of scar symmetry over preservation of native breast skin. These findings are consistent with previous studies that surveyed non-crowdsourced participants, which demonstrates the potential for crowdsourcing to be used to better understand the general public's preferences in plastic surgery.

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COVID-19 led to work hour reductions and layoffs for many Americans with wage/salary jobs. Some gig work, however, which is usually considered precarious, remained available. We examine whether people doing gig microtasks right before the pandemic increased their microtask hours during COVID-19 and whether those changes helped them financially. Using data from workers on Amazon's Mechanical Turk platform from February, March, and April of 2020, we find that roughly one third of existing workers increased their microtask hours. Increases were larger for people who lost household income or wage/salary hours. Spending more time on microtasks, however, did little to help workers financially. Furthermore, the people most reliant on microtasks before the pandemic had worse financial outcomes than others. In short, even though microtask work might seem like a good way for people to recoup lost income during the pandemic, it was of limited utility even for the experienced workers in our sample.

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OBJECTIVE: There are limited data to guide the interpretation of scores on measures of eating-disorder psychopathology among underrepresented individuals. We aimed to provide norms for the Eating Disorder Examination-Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) across racial/ethnic, gender, and sexual identities, and sexual orientations and their intersections by recruiting a diverse sample of Amazon MTurk workers (MTurkers; N = 1782). METHOD: We created a comprehensive, quantitative assessment of racial/ethnic identification, gender identification, sex assigned at birth, current sexual identification, and sexual orientation called the Demographic Assessment of Racial, Sexual, and Gender Identities (DARSGI). We calculated normative data for each demographic category response option. RESULTS: Our sample was comprised of 68% underrepresented racial/ethnic identities, 42% underrepresented gender identities, 13% underrepresented sexes, and 49% underrepresented sexual orientations. We reported means and standard deviations for each demographic category response option and, where possible, mean estimates by percentile across intersectional groups. EDE-Q Global Score for a subset of identities and intersections in the current study were higher than previously reported norms for those identities/intersections. DISCUSSION: This is the most thorough reporting of norms for the EDE-Q and CIA among racial/ethnic, sexual, and gender identities, and sexual orientations and the first reporting on multiple intersections, filling some of the gaps for commonly used measures of eating-disorder psychopathology. These norms may be used to contextualize eating-disorder psychopathology reported by underrepresented individuals. The data from the current study may help inform research on the prevention and treatment of eating-disorder psychopathology in underrepresented groups. PUBLIC SIGNIFICANCE: We provide the most thorough reporting on racial/ethnic, sexual, and gender identities, and sexual orientations for the Eating Disorder Examination - Questionnaire and Clinical Impairment Assessment, and the first reporting on intersections, which fills some of the gaps for commonly used measures of eating-disorder psychopathology. These norms help inform research on the prevention and treatment of eating-disorder psychopathology in underrepresented groups.

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Introduction: Prevalence of alcohol and marijuana use is highest in young adulthood and an increasing number of young adults report simultaneous alcohol and marijuana (SAM) use, which is consistently linked with numerous negative consequences. To better understand reasons for engaging in SAM use and to refine measurement of subjective effects of SAM use, this study aimed to identify (1) how young adults describe subjective experiences during a SAM use occasion and (2) how language describing subjective effects changes as a function of level of alcohol and marijuana use. Methods: Using Amazon's Mechanical Turk (MTurk), 323 participants (53.6% women, 68.4% White, M age = 23.0 years) who reported past-month heavy episodic drinking and past-month SAM use were asked to list words to describe how they feel when using only alcohol, only marijuana, and various combinations of alcohol and marijuana. Results: SAM use language varied as a function of age and substance use behavior but was not associated with sex or race. Large differences in the terms used to describe subjective effects were observed when comparing different combinations of alcohol and marijuana use; most notably the term "cross-faded" appeared primarily when engaging at the heaviest combinations of alcohol and marijuana. Conclusion: Young adults have a wide range of vocabulary for describing subjective effects of SAM use, and subjective effects vary as a function of the level of each substance used. Future research should consider integrating such contemporary language when measuring subjective effects of SAM use.

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Hoarding disorder (HD) is a psychiatric condition characterized by difficulty discarding items and accumulation of clutter. Although studies have established the negative impact of HD and compulsive hoarding behavior, fewer have examined the impact on quality of life (QoL) of hoarding behavior independent of obsessive-compulsive disorder (OCD). Moreover, specific aspects of QoL such as success in work/academics or satisfaction with interpersonal relationships have not been well-investigated. In this study, we examined, in a sample of 2100 adult participants obtained from Amazon Mechanical Turk, the relationships between hoarding, OCD, and depression symptomatology and four QoL domains (success, enrichment, environment, and family) derived from a factor analysis of the Quality of Life Inventory (QoLI). We performed linear regressions to examine associations between psychiatric symptomatology and QoL domains and then conducted mediation analyses to investigate the role of depressive symptomatology in the identified relationships. We found that while hoarding and obsessive-compulsive symptoms were both negatively associated with QoL, they were associated with different domains [hoarding was significantly associated (p < 0.05) with total QoL and all domains and uniquely associated with environment and family QoL compared to obsessive-compulsive symptoms], whereas obsessive-compulsive symptoms were only significantly associated with total, success, and enrichment QoL. However, when depressive symptoms were included in the model, hoarding no longer accounted for significant variance in the total, environment, or family QoL domains (p > 0.05), and was less strongly associated with success or enrichment. Mediation analyses confirmed the role of depression as a complete mediator of hoarding's effect on total, environment, and family QoL, and as a partial mediator of hoarding's effect on success and enrichment QoL. Further examination of the relationship between hoarding symptoms and QoL in those with mild, moderate, and severe depression indicated that in those with more severe depression, hoarding was associated with improved QoL, indicating a possible buffering or compensatory effect. The findings suggest a differential impact of hoarding and obsessive-compulsive symptoms on QoL and emphasize the importance of considering co-morbid depressive symptoms in designing more targeted interventions. Future studies should continue to investigate these complex relationships, given the high co-morbidity of hoarding and depression.

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BACKGROUND: The lack of publicly available and culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults' disease prevention and health promotion priorities presents a major challenge for researchers and developers who want to create and test personalized tools built on and aligned with those priorities. Personalization depends on prediction and performance data. A recommender system (RecSys) could predict the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users via a novel smartphone app. However, early in a user's experience, a RecSys can face the "cold start problem" of serving untailored and irrelevant content before it learns user preferences. For underserved African American and Hispanic populations, who are consistently being served health content targeted toward the White majority, the cold start problem can become an example of algorithmic bias. To avoid this, a RecSys needs population-appropriate seed data aligned with the app's purposes. Crowdsourcing provides a means to generate population-appropriate seed data. OBJECTIVE: Our objective was to identify and test a method to address the lack of culturally specific preventative personal health data and sidestep the type of algorithmic bias inherent in a RecSys not trained in the population of focus. We did this by collecting a large amount of data quickly and at low cost from members of the population of focus, thereby generating a novel data set based on prevention-focused, population-relevant health goals. We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American/Black adult respondents, using Amazon Mechanical Turk (MTurk). METHODS: MTurk provided the crowdsourcing platform for a web-based survey in which respondents completed a personal profile and a health information-seeking assessment, and provided data on family health history and personal health history. Respondents then selected their top 3 health goals related to preventable health conditions, and for each goal, reviewed and rated the top 3 information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. This paper reports the article ratings because our intent was to assess the benefits of crowdsourcing to seed a RecSys. The analysis of the data from health goals will be reported in future papers. RESULTS: The MTurk crowdsourcing approach generated 985 valid responses from 485 (49%) self-identified Hispanic and 500 (51%) self-identified non-Hispanic African American adults over the course of only 64 days at a cost of US $6.74 per respondent. Respondents rated 92 unique articles to inform the RecSys. CONCLUSIONS: Researchers have options such as MTurk as a quick, low-cost means to avoid the cold start problem for algorithms and to sidestep bias and low relevance for an intended population of app users. Seeding a RecSys with responses from people like the intended users allows for the development of a digital health tool that can recommend information to users based on similar demography, health goals, and health history. This approach minimizes the potential, initial gaps in algorithm performance; allows for quicker algorithm refinement in use; and may deliver a better user experience to individuals seeking preventative health information to improve health and achieve health goals.

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Prior studies have examined how stress and the family environment affect overeating, but less is known about how COVID-19 stressors and family health may affect overeating during the COVID-19 pandemic. The research questions included: (1) Did COVID-19-related stressors increase the risk for overeating among adults in the United States? (2) Did family health protect against overeating during a pandemic? The sample included 443 participants aged 18 years and older living in the United States who were recruited via Amazon Mechanical Turk. Stata version 16 was used to analyze the data using multiple linear regression. The results indicate that one year into the pandemic, COVID-19 stressors were associated with increased overeating, even after adjusting for overeating before the pandemic. More family health resources were associated with less overeating. These results indicate that although COVID-19 stressors were associated with overeating, greater family health resources helped prevent overeating. Interventions and policies that aim to increase health resources for families may be particularly beneficial at preventing overeating and obesity in the face of long- and short-term stress.

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