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Online surveys have become a key tool of modern health research, offering a fast, cost-effective, and convenient means of data collection. It enables researchers to access diverse populations, such as those underrepresented in traditional studies, and facilitates the collection of stigmatized or sensitive behaviors through greater anonymity. However, the ease of participation also introduces significant challenges, particularly around data integrity and rigor. As fraudulent responses—whether from bots, repeat responders, or individuals misrepresenting themselves—become more sophisticated and pervasive, ensuring the rigor of online surveys has never been more crucial. This article provides a comprehensive synthesis of practical strategies that help to increase the rigor of online surveys through the detection and removal of fraudulent data. Drawing on recent literature and case studies, we outline several options that address the full research cycle from predata collection strategies to validation post data collection. We emphasize the integration of automated screening techniques (eg, CAPTCHAs and honeypot questions) and attention checks (eg, trap questions) for purposeful survey design. Robust recruitment procedures (eg, concealed eligibility criteria and 2-stage screening) and a proper incentive or compensation structure can also help to deter fraudulent participation. We examine the merits and limitations of different sampling methodologies, including river sampling, online panels, and crowdsourcing platforms, offering guidance on how to select samples based on specific research objectives. Post data collection, we discuss metadata-based techniques to detect fraudulent data (eg, duplicate email or IP addresses, response time analysis), alongside methods to better screen for low-quality responses (eg, inconsistent response patterns and improbable qualitative responses). The escalating sophistication of fraud tactics, particularly with the growth of artificial intelligence (AI), demands that researchers continuously adapt and stay vigilant. We propose the use of dynamic protocols, combining multiple strategies into a multipronged approach that can better filter for fraudulent data and evolve depending on the type of responses received across the data collection process. However, there is still significant room for strategies to develop, and it should be a key focus for upcoming research. As online surveys become increasingly integral to health research, investing in robust strategies to screen for fraudulent data and increasing the rigor of studies is key to upholding scientific integrity.

Survey research has the potential to elevate the experiences and opinions of marginalized populations. The rising number of bot attacks, a method of participant fraud that creates multiple records in survey data using automated software, threatens to drown out those voices and produce inaccurate findings. Rapid identification and mitigation of bot attacks are vital; however, there is limited guidance for researchers on scalable approaches to address this problem. This study aimed to assess how well recommended methods detect fraud using an epidemiological diagnostic test framework to inform web-based survey researchers on how best to identify and shut down bot attacks. We analyzed data from a cross-sectional web-based statewide survey on access to pediatric subspecialty care in California that used Qualtrics survey software. Caregivers of children with chronic conditions were recruited through family resource centers (FRCs), nonprofit agencies serving families with developmental delays and chronic medical conditions. The survey was sent out to 17 FRCs, whose staff distributed anonymous links to their clients through listservs and flyers. Respondents who completed the survey received a US $30 gift card. Prior to launch, we designed a protocol to identify and respond to bot attacks and reviewed responses for markers of fraudulent activity. If markers were identified or there was a spike in responses, a senior member of our research team reviewed patterns among all submitted surveys for each FRC to look for signs of bot attacks. We calculated epidemiologic measures of diagnostic test accuracy, such as sensitivity, specificity, positive predictive value, and negative predictive value, which describe a test's ability to distinguish \"disease\" (in this case, fraudulent records) from normal cases, to better understand the utility of recommended strategies to identify bot attacks. We received 646 valid survey records and 905 fraudulent records resulting from bot attacks. The primary indicator of a bot attack was a sudden spike in responses to the survey. Differences in demographics and outcomes, including wait times for pediatric subspecialty care and use of health care services, between the valid and fraudulent data indicated that failure to remove fraudulent records would have substantially altered the survey results. Most recommended methods in the literature for identifying fraudulent responses had low sensitivity to detect bot attacks, and only 2 were better than chance alone at correctly identifying bot attacks. Combinations of fraud markers and blocks of repeated responses were particularly useful to identify bot attacks. Fraudulent data entry using bots is increasing in survey research. Sharing flexible protocols to identify and mitigate them in a way that is responsive to their ever-changing nature is vital to ensuring that researchers elevate the voices of real people within survey research to inform policy and programmatic discussions.

Smartphone-based digital phenotyping uses built-in sensors and usage patterns to passively capture behavioral and environmental data relevant to health and has been applied extensively in mental health and chronic disease contexts. This review synthesizes studies that use smartphone-based digital phenotyping, defined as approaches that rely exclusively on onboard smartphone sensors to characterize specific health conditions. To our knowledge, this work provides the most comprehensive cross-condition synthesis of smartphone-based digital phenotyping to date, spanning mental health, physical health, and substance use disorders (SUDs), and highlighting common practices, gaps, and opportunities for future research. We conducted a scoping review of English-language, peer-reviewed papers published between 2012 and 2025 in Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed using terms such as \"mobile sensing\" and \"digital phenotyping.\" Eligible papers used onboard smartphone sensors to assess health and went beyond self-report. Studies that did not rely on smartphone auxiliary sensing modalities or digital phenotyping were excluded. We performed a descriptive synthesis of study characteristics, sensors, and health domains. Of 111 papers identified, 65 met inclusion criteria. Most studies were observational and relied on passive sensing. Sample sizes ranged from fewer than 10 to over 18,000 participants, with a median of 52 (IQR=26-126). Mental health conditions were most frequently examined, including depression (n=16), bipolar disorder (n=11), stress or anxiety (n=10), and schizophrenia (n=8). Less commonly studied conditions included SUDs (n=7), Parkinson disease (n=4), and sleep apnea (n=2). Sensor streams varied widely and included diverse passive smartphone data sources capturing mobility, communication, device usage, environmental context, and user interaction patterns. Ground-truth measurements most commonly relied on validated clinical scales (eg, Patient Health Questionnaire-9, Young Mania Rating Scale [YMRS], and Pittsburgh Sleep Quality Index; n=41), followed by ecological momentary assessments (n=18), clinician-confirmed diagnoses (n=9), and physiological measures such as polysomnography (n=3). Across studies, recurring methodological limitations included incomplete or inconsistent sensor descriptions, limited reporting of data quality (eg, sampling rates and missingness), and heterogeneous validation practices. These issues limit comparability and reproducibility and underscore the need for clearer reporting standards and greater data availability. This scoping review provides the first comprehensive synthesis of smartphone-only digital phenotyping studies spanning mental health, physical health, and SUDs. Unlike prior reviews, this work maps behavioral associations derived exclusively from smartphone sensors across a broad range of health domains. The primary contribution of this review lies in its consolidation of behavioral associations observed across studies, enabling researchers to correlate new findings to the existing evidence base and identify opportunities for replication, extension, or clinical translation. Collectively, these findings highlight both the promise of smartphone-based digital phenotyping in real-world settings and the need for improved standardization to support translation into clinical and public health applications.

Preliminary research has suggested that internet use data could offer digital signals of early disease and has the potential to facilitate early detection and improve patient outcomes. However, there are significant challenges in linking individual-level internet use data with health outcomes. One key aspect is that the public might not be willing to share data for research or that selective data sharing might create bias in datasets and increase inequalities. Our study aimed to investigate the willingness of the public to share their internet use data for medical research and to identify key criteria that affect willingness to share. We conducted a web-based, cross-sectional online survey with 2390 UK adults with and without a history of cancer, heart disease, and depression using quota sampling. Participants were randomly assigned to explore willingness to share different types of internet use data for 1 of 3 health conditions (cancer, heart disease, and depression) and for provision of a pictorial example of internet use data. Logistic regression analysis (α=.05) for each condition was used to determine key factors of willingness to share, including sociodemographics and attitudes toward sharing. Open-ended comments regarding facilitators of sharing and concerns were analyzed thematically. Willingness to share internet use data was high across conditions (74%-77%, 95% CI 70.5%-80.3%), especially for health app data (73%-76%, 95% CI 69.8%-79.1%). The pictorial example of browsing history did not affect willingness to share. For all conditions, factors consistently associated with willingness to share were perceived benefits (odds ratios [ORs] 5.692-8.850; all P<.001) and concerns (ORs 0.343-0.432; all P<.001). Key concerns were data privacy, potential for misuse, and lack of relevance. Suggestions to increase willingness to share included contributing to society and research, data security assurances, clarification of research purposes, and monetary incentives. Familiarity with internet use data was related to lower willingness to share for heart disease detection (OR 0.740, 95% CI 0.561-0.976). Asian ethnicity was associated with lower willingness to share internet use for cancer detection (OR 0.234, 95% CI 0.076-0.723). Younger age (OR 0.975, 95% CI 0.951-0.999) and male gender (OR 2.615, 95% CI 1.511-4.526) were associated with higher willingness to share data for depression detection. This first large-scale assessment of public willingness to share internet use data for early disease detection adds novel insights by comparing conditions and examining sociodemographic factors alongside perceived benefits and risks. It highlights that understanding of internet data is limited yet willingness to share for research is high. Clear communication of benefits, strong privacy protections, and incentives may increase participation and reduce bias. The findings inform consent design, targeted outreach to underrepresented groups, and data governance for safe use of personal digital data. Future research should focus on improving public communication, particularly among less willing groups at risk of inequality.

With the development of digital health platforms, patients with breast cancer are increasingly relying on web-based resources to search for disease-related information. Proper usage of web-based health information by patients with breast cancer is crucial for understanding disease information and participating in treatment decisions. However, in the face of the large amount and complexity of information, it is still unclear how patients can make psychological adjustments and behavioral responses. Problems such as variable information quality and conflicting information are also affecting the cognitive and treatment decision-making process of patients with breast cancer. This study aims to explore the real experiences of Chinese patients with breast cancer in their search for web-based health information from a phenomenological perspective, providing insights for optimizing future web-based health information support for patients. This qualitative study used semistructured, in-depth face-to-face interviews to collect data. Through purposive and convenience sampling, 18 female patients with breast cancer were recruited from a tertiary cancer hospital in China. The data saturation principle was observed to determine the endpoint of data collection. The collected data were analyzed using thematic analysis. From 18 original interview documents, three themes and 11 subthemes were categorized as follows: (1) driving force of information search (emotion-based information search, problem-solving-oriented information search), (2) cognitive judgments amidst the information fog (interweaving of multichannel information, judgment of information authenticity, information applicability assessment, cognitive confusion in the context of information conflict, and construction of information meaning), and (3) adaptation under the pressure of web-based information (transform information into action, emotional regulatory coping, build a support network, and acceptance and adjustment of expectations). This study reveals that the experiences of patients with breast cancer within web-based health information environments resemble an information navigation journey. Patients continuously search, evaluate, and adjust within the sea of information to maintain cognitive clarity and emotional equilibrium. The findings offer valuable insights for clinical health care providers, health information platform developers, and policymakers. They can help optimize digital health services and design personalized information support that better meets patients' needs.

Mixed methods research is essential to development of patient-reported outcome measures, digital technology, and endpoint selection for clinical drug trials and to advance clinical care when complex health-related experiences cannot be fully understood by quantitative or qualitative approaches alone. New technology and opportunities for remote data collection have changed the ways in which qualitative and quantitative data can be collected, enabling researchers to capture human experiences in ways not previously possible. This paper describes Perspective Mapping, a new online interviewing technique that uses mind mapping software to capture in-depth qualitative data inside a quantitative measurement framework to understand and measure individual experiences. The objective of this tutorial is to review the theoretical underpinnings, present instructions for study design and implementation, and address strengths, limitations, and potential applications of this technique in health and behavioral sciences. During videoconferencing interviews, mind-mapping software is used to visually depict experiences. Structured concept maps are cocreated in real time with participants, focusing on building detailed narrative descriptions about experiences and categorizing these within a predefined quantitative framework, such as the relative importance of different experiences relevant to a phenomenon. The approach combines semistructured interviewing with technology-enhanced card-sorting techniques, allowing participants to define and prioritize what matters most. This method ensures narrative richness alongside structured data collection, facilitating deeper understanding of phenomena. Perspective Mapping emphasizes participant engagement in data generation and analysis and enables the simultaneous collection of qualitative narratives and quantitative assessment of key concepts. The variations of the technique have been successfully applied in research on chronic illness, symptom burden, and digital health technology. Advantages of the approach include systematic collection of qualitative data, transparent and structured data outputs, real-time data validation, and the ability to return maps to participants as a form of reciprocity. Feasibility factors, such as interviewer capabilities, participant literacy, interview duration, and technology resources must be considered. Perspective Mapping offers an innovative and engaging way to gather complementary qualitative and quantitative data remotely. By blending qualitative depth with quantitative structure, the technique supports richer, more actionable insights for health research, policy, and beyond. This technique holds promise for applications in health, psychology, education, and other social sciences where comprehensive understanding of experiences is essential.

Currently, the rapid aging of global population, especially in low- and middle-income countries, is placing changing demands on health care systems. The preparation of the population for adequate eHealth literacy and good digital health is one of the challenges of social policy. The willingness to understand eHealth literacy and telemedicine use across different age groups of the population will help identify loopholes and bottlenecks in the implementation and help to develop appropriate solutions. Currently, studies on the status of eHealth literacy across different age ranges remain limited and scarce. In this study, we aimed to investigate the prevalence and factors associated with adequate eHealth literacy, including attitudes toward eHealth literacy and willingness to use telemedicine as an example of digital technology. We focused on the comparison between older people (aged ≥60 years) and younger adult groups in Thailand, a middle-income country. We conducted a cross-sectional, observational study from January 2021 to July 2021. A total of 400 participants who visited the outpatient department of Siriraj Hospital were recruited and completed questionnaires collecting demographic information, frequency of internet use, and devices used for accessing the internet. eHealth literacy was assessed using the eHAELS (eHealth Literacy Scale) questionnaire. We also explored the participants' attitude and willingness to use telemedicine. We applied univariable logistic regression analysis to elucidate the factors associated with eHealth literacy and willingness to use telemedicine. Our study revealed that the older participants had lower level of eHealth literacy compared to younger participants. Using an eHAELS score ≥26 points to define 'adequate eHealth literacy,' 74.0% (n=97) of older adults compared to 22.7% (n=61) of younger adults had inadequate eHealth literacy. Only 19.8% (n=26) of older adults, compared to 65.1% (n=175) of younger adults showed high levels of eHealth literacy defined by exploring each item using the eHEALS tool. The items with the lowest level of eHealth literacy among older adults pertained to confidence in finding and applying health information for self-care and in using information from the internet for making health decisions. In terms of attitude and interest toward telemedicine use, confidence in security, perceived convenience of telemedicine, and adequate eHealth literacy were the three strongest factors associated with willingness to use telemedicine, with odds ratios (ORs) of 5.90 (95% CI 3.43-10.15), 5.43(95% CI 3.12-9.43), and 4.45 (95% CI 2.60-7.62), respectively. Additionally, the younger adults were more likely to be interested in using telemedicine with an OR of 2.02 (95% CI 1.21-33.37). Our study addressed the low level of eHealth literacy, with more concerning figures among older adults compared to younger adults in a middle-income country. The willingness to adopt digital technologies related strongly to level of eHealth literacy. This information may be beneficial for guiding further improvements and promoting digital health in low- and middle-income settings facing the challenges of an aging population.

Mammography screening uptake in Singapore remains below 40% despite campaigns and subsidies. Natural language processing (NLP) can extract nuanced attitudes from free text that fixed response options miss, revealing latent factors influencing breast cancer (BC) screening behavior. This study characterized women's attitudes toward mammography using mixed methods data, examined associations between BC awareness and screening willingness, and identified barriers and facilitators through NLP of free-text responses. We conducted a cross-sectional study within the Breast Screening Tailored for Her multicenter cohort in Singapore (October 2021-December 2023). In total, 4169 women aged 35-59 years (median 48, IQR 43-54) were recruited via convenience sampling (3 hospitals and 2 polyclinics). Participants completed online structured questionnaires on demographics and screening history, then a BC education quiz with feedback. Participants answering >80% correctly were classified as \"BC-aware.\" Posteducation, participants reported screening willingness (motivated or neutral) with optional free-text explanations. Logistic regression models (adjusted for study site, age, ethnicity, marital status, housing, and education) examined the associations with willingness. For 3819 English-language respondents, biterm topic modeling identified themes and sentiment analysis quantified emotional tone. Statistical significance: α=.05. Overall, 79% (3287/4169) were BC-aware, and 94% (3908/4169) reported increased motivation posteducation. BC-aware women had higher screening motivation than BC-unaware women (adjusted odds ratio [aOR] 2.88, 95% CI 2.19-3.80; P<.001). Motivation was higher among those with larger public housing (OR 1.81, 95% CI 1.30-2.50; P<.001) and private housing vs 1-3 room units (OR 2.69, 95% CI 1.75-4.13; P<.001), married vs not separated, divorced, or widowed (OR 2.38 [inverse of 0.42], 95% CI 1.75-3.13; P<.001), and prior screening attendance (OR 3.49, 95% CI 2.71-4.50; P<.001). Women who disagreed that mammography was expensive had higher motivation (aOR 1.94, 95% CI 1.50-2.50; P<.001). Among 3819 English respondents, 94% (3579/3819) were motivated and 6% (240/3819) neutral. Free-text responses came from 34% (1220/3579) of motivated and 64% (153/240) of neutral participants. Biterm topic modeling revealed motivated participants emphasized early detection benefits, health awareness, BC risk, and logistics; neutral participants focused on mammography pain experiences and cost barriers. Mean sentiment was 0.207 (range: -1.00 to 1.65), with motivated participants displaying more positive sentiments than neutral participants (linear regression, P<.001). Identical words carried different emotional tones across subgroups: \"health\" had positive sentiment among motivated participants (mean difference, Welch t tests P<.05) but negative sentiment among neutral participants. Word frequency analysis showed motivated participants used positive-sentiment words (\"better,\" \"cure,\" and \"prevention\"). Neutral participants emphasized negative words (\"painful\" and \"uncomfortable\"). Integrating quantitative surveys with NLP revealed that the same screening concepts are emotionally framed differently by motivated vs neutral women, a finding missed by knowledge- or intent-focused approaches alone. In practice, these findings support the need for emotionally tailored BC education and prevention strategies.

Digital recruitment methods offer opportunities to address challenges in clinical research participation, particularly in neurology. However, the impact of digital approaches across socioeconomic and demographic groups remains inadequately understood. This study investigates the influence of sociodemographic factors on recruitment and attrition in a remote neurological research cohort, mapping participation pathways and identifying disparities to inform inclusive digital strategies. We conducted a nonexperimental, observational longitudinal cohort study at Mayo Clinic using patient-portal invitations between March and July 2024 as part of a remote speech capture study. Eligibility criteria included age 18 years and older, US residence, and English proficiency. Of 5846 invited patients, progression was tracked across checkpoints (invitation, eligibility screening, electronic consent, and task completion) using Epic (Epic Systems Corporation) to obtain demographic information, Qualtrics (Qualtrics, LLC) for screening, PTrax (a Mayo Clinic-developed Participant Tracking System) for consent tracking, and the recording platform. Socioeconomic context was assessed using the Housing-based Socioeconomic Status (HOUSES) index, where higher values indicate higher socioeconomic status, and the Area Deprivation Index (ADI), where higher values reflect greater neighborhood disadvantage. Data diagnostics included Anderson-Darling tests for non-normality and Little missing completely at random (MCAR) test to characterize missingness. Associations between participation outcomes and age, sex, urbanicity, and socioeconomic indices were examined using nonparametric tests. Exact P values and 95% CIs are reported. Analyses were conducted using BlueSky Statistics (BlueSky Statistics, LLC) and the Python SciPy package. Overall, 415 out of 5846 participants (7.1%) completed all study requirements. Completers were older (median age 66.4, IQR 56.0-72.5; 95% CI 65.1-67.6 years) than noncompleters (median age 62.8, IQR 47.5-72.7; 95% CI 62.2-63.2 years; P<.001). Participants from more socioeconomically disadvantaged neighborhoods were less likely to respond (invitation nonresponder median ADI 45.0, IQR 29.0-63.0 vs interested median ADI 42.0, IQR 27.0-59.0; P<.001), and completers had slightly lower ADI ranks than noncompleters (median 41.0, IQR 27.0-56.0 vs median 44.5, IQR 28.0-62.0; P=.04). Urban participants enrolled faster (median 32.0, IQR 9.0-58.0; 95% CI 31.0-37.0 days) than rural (median 41.0, IQR 22.0-65.0; 95% CI 37.0-49.0 days; P=.01). Female participants responded slower (median 38.5, IQR 14.8-66.3; 95% CI 35.0-41.0 days) than males (median 32.0, IQR 8.0-57.5; 95% CI 29.0-38.0 days; P=.01). No significant differences were observed for the HOUSES index, and device type was unrelated to completion or timelines. Missingness for key variables was completely at random (MCAR χ²3=3.45; P=.24). Digital recruitment does not overcome traditional barriers to participation and may introduce new disparities related to age, urbanicity, and neighborhood disadvantage. These findings inform inclusive digital research strategies, including multichannel outreach, age-specific engagement, and rural technical support. This study applies longitudinal pathway analysis to digital neurology recruitment, offering actionable insights for improving inclusivity in remote research.

The World Health Organization recommends that countries routinely collect data on the behavioral and social drivers (BeSD) of vaccination to inform public health interventions that increase vaccine uptake. There is a need to identify data collection methods that can rapidly and inexpensively collect representative data, particularly in low- and middle-income countries. This study aimed to understand BeSD drivers of vaccination in the Philippines and assess the trade-offs between survey methods. We compared responses to household, mobile, and online surveys in terms of demographics, vaccination status, responses to BeSD questions, and cost. We conducted concurrent household, mobile (SMS text messaging and interactive voice response), and online surveys among caregivers of children 2 years of age and below in Regions V and XII of the Philippines, with sampling differing by survey method. We assessed, for each survey method, (1) respondent demographics (sex, age, region, and socioeconomic status) and (2) the weighted proportion of responses from caregivers of children who received at least one dose of diphtheria-pertussis-tetanus (DPT)-containing vaccine. We estimated the weighted proportion of each BeSD survey response option and calculated the financial cost (monetary outlays) per survey response from an implementer's perspective by summing the costs incurred in each survey method and dividing by the number of responses received. We surveyed a total of 1201 household respondents, 2153 mobile respondents, and 398 online respondents from January to March 2025. We found that online and mobile survey respondents were more likely to be male and have completed high school than household survey respondents. The weighted proportion of respondents indicating that their child had received at least one dose of DPT vaccine was 91.8% (n=1090; 95% CI 90%-93.3%) for the household survey, 90.3% (n=1853) for the mobile survey, and 85% (n=346) for the online survey. With regard to vaccine demand, more than 85% of respondents in each survey method indicated that vaccines are very important, very safe, supported by family, and that they knew where to bring a child for vaccination. More than 30% of mobile and online survey respondents indicated that it was not easy to pay for vaccination. The financial cost to conduct the survey per survey response was US $2.61 for the online survey, US $6.93 for the mobile survey, and US $29.38 for the household survey. In the Philippines, household, mobile, and online survey methods reached caregivers of children who were unvaccinated against DPT, and these proportions were similar across survey methods. BeSD responses indicated high vaccine demand and challenges in caregivers' cost to access vaccination. Determining the most appropriate survey method depends on trade-offs between representativeness and costs. However, areas with strong connectivity and high mobile device ownership can consider mobile and online methods as a lower-cost alternative to rapidly collect BeSD data.