Enriching Tiger Nut Milk with Sodium Caseinate and Xanthan Gum Improves the Physical Stability and Consumer Acceptability

Tiger nut milk (TNM) shows limited colloidal stability, which affects consumer acceptability in many parts of the world where tiger nut is cultivated. In this study, addition of proteins and hydrocolloids was used for improving the stability, and the impact on physical properties and consumer acceptance is reported. Enriching TNM by 3 g/100 g sodium caseinate and 0.1 g/100 g xanthan gum successfully impeded creaming and serum formation and resulted in a decrease of the instability index from 0.408 ± 0.023 to 0.015 ± 0.00 after applying forced sedimentation at 3000 x g for 2 h. After TNM enrichment, the viscosity of TNM increased from 3.0 ± 0.10 mPa.s to 285 ± 18 mPa.s which remained stable at elevated storage temperature. Flash profiling of TNM resulted in emerging descriptors namely sweet, sediment, watery, raw. Hedonic assessment by 82 consumers showed that plain TNM had the lowest rating concerning particular sensory attributes and acceptance. Enrichment resulted in more viscous, sweet and thick TNM products, leading to higher consumer ratings of attributes and acceptability. Thus, enriching TNM by sodium caseinates and xanthan gum is promising for improving the dispersion stability and consumer acceptance.

Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality

Background There is a robust market for mobile health (mHealth) apps focused on self-guided interventions to address a high prevalence of mental health disorders and behavioral health needs in the general population. Disseminating mental health interventions via mHealth technologies may help overcome barriers in access to care and has broad consumer appeal. However, development and testing of mental health apps in formal research settings are limited and far outpaced by everyday consumer use. In addition to prioritizing efficacy and effectiveness testing, researchers should examine and test app design elements that impact the user experience, increase engagement, and lead to sustained use over time. Objective The aim of this study was to evaluate the objective and subjective quality of apps that are successful across both research and consumer sectors, and the relationships between objective app quality, subjective user ratings, and evidence-based behavior change techniques. This will help inform user-centered design considerations for mHealth researchers to maximize design elements and features associated with consumer appeal, engagement, and sustainability. Methods We conducted a user-centered design analysis of popular consumer apps with scientific backing utilizing the well-validated Mobile Application Rating Scale (MARS). Popular consumer apps with research support were identified via a systematic search of the App Store iOS (Apple Inc) and Google Play (Google LLC) and literature review. We evaluated the quality metrics of 19 mental health apps along 4 MARS subscales, namely, Engagement, Functionality, Aesthetics, and Information Quality. MARS total and subscale scores range from 1 to 5, with higher scores representing better quality. We then extracted user ratings from app download platforms and coded apps for evidence-based treatment components. We calculated Pearson correlation coefficients to identify associations between MARS scores, App Store iOS/Google Play consumer ratings, and number of evidence-based treatment components. Results The mean MARS score was 3.52 (SD 0.71), consumer rating was 4.22 (SD 0.54), and number of evidence-based treatment components was 2.32 (SD 1.42). Consumer ratings were significantly correlated with the MARS Functionality subscale (r=0.74, P<.001), Aesthetics subscale (r=0.70, P<.01), and total score (r=0.58, P=.01). Number of evidence-based intervention components was not associated with MARS scores (r=0.085, P=.73) or consumer ratings (r=–0.329, P=.16). Conclusions In our analysis of popular research-supported consumer apps, objective app quality and subjective consumer ratings were generally high. App functionality and aesthetics were highly consistent with consumer appeal, whereas evidence-based components were not. In addition to designing treatments that work, we recommend that researchers prioritize aspects of app design that impact the user experience for engagement and sustainability (eg, ease of use, navigation, visual appeal). This will help translate evidence-based interventions to the competitive consumer app market, thus bridging the gap between research development and real-world implementation.

The Implications of COVID-19 on Chinese Consumer Preferences for Lamb Meat

This study assessed if Chinese consumer attitudes towards a range of lamb attributes (such as origin, food safety, appearance, taste, price), and their opinions of New Zealand lamb (9- and 7-point Likert scales, respectively), had changed since the outbreak COVID-19. The same survey was carried out in Shanghai and Beijing pre (December 2018) and post COVID-19 (November 2020), ~9 months after China’s initial outbreak, with 500 and 523 consumers, respectively. From December 2018 to November 2020, there was an increase in the proportion of Chinese consumers purchasing red meat online or from a butcher, and cooking their lamb well-done. In contrast, there were minimal differences in Chinese consumer ratings between December 2018 and November 2020 for different lamb attributes and opinions of New Zealand lamb. Cluster analysis revealed that many consumers (140 in December 2018/376 in November 2020) used only a small portion of the high end of the scale when rating lamb attributes, resulting in little differences between the attributes. This study suggests COVID-19 has enhanced some food safety related behaviors but had little effect on Chinese opinions and preferences for New Zealand lamb attributes. It also highlights that survey design should be carefully considered when collecting responses from Chinese consumers.

Exploring the Factors Affecting the Purchase Likelihood of e-Tailer Private Labels.

<p><b>In today’s extremely competitive retail marketplace environment, developing and managing profitable private label offerings has become significant for most retailing companies. The purpose of this paper is to understand the influence of e-tailer reputation, product manufacturing country favourability, and online consumer rating on the purchase likelihood of e-tailer private labels. We find that product online consumer ratings positively impact the purchase intention of the private labels. E-tailer reputation has shown a positive impact on the future purchase intentions whereas, country favourability’s effect is found at the time of actual purchase of e-tailer private labels. Being the first research to study the impact of country favourability, e-tailer reputation on e-tailer private labels, this paper offers some insights to the e-tailers. </b></p>

Exploring the Factors Affecting the Purchase Likelihood of e-Tailer Private Labels.

<p><b>In today’s extremely competitive retail marketplace environment, developing and managing profitable private label offerings has become significant for most retailing companies. The purpose of this paper is to understand the influence of e-tailer reputation, product manufacturing country favourability, and online consumer rating on the purchase likelihood of e-tailer private labels. We find that product online consumer ratings positively impact the purchase intention of the private labels. E-tailer reputation has shown a positive impact on the future purchase intentions whereas, country favourability’s effect is found at the time of actual purchase of e-tailer private labels. Being the first research to study the impact of country favourability, e-tailer reputation on e-tailer private labels, this paper offers some insights to the e-tailers. </b></p>

Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality (Preprint)

BACKGROUND There is a robust market for mobile health (mHealth) apps focused on self-guided interventions to address a high prevalence of mental health disorders and behavioral health needs in the general population. Disseminating mental health interventions via mHealth technologies may help overcome barriers in access to care and has broad consumer appeal. However, development and testing of mental health apps in formal research settings are limited and far outpaced by everyday consumer use. In addition to prioritizing efficacy and effectiveness testing, researchers should examine and test app design elements that impact the user experience, increase engagement, and lead to sustained use over time. OBJECTIVE The aim of this study was to evaluate the objective and subjective quality of apps that are successful across both research and consumer sectors, and the relationships between objective app quality, subjective user ratings, and evidence-based behavior change techniques. This will help inform user-centered design considerations for mHealth researchers to maximize design elements and features associated with consumer appeal, engagement, and sustainability. METHODS We conducted a user-centered design analysis of popular consumer apps with scientific backing utilizing the well-validated Mobile Application Rating Scale (MARS). Popular consumer apps with research support were identified via a systematic search of the App Store iOS (Apple Inc) and Google Play (Google LLC) and literature review. We evaluated the quality metrics of 19 mental health apps along 4 MARS subscales, namely, Engagement, Functionality, Aesthetics, and Information Quality. MARS total and subscale scores range from 1 to 5, with higher scores representing better quality. We then extracted user ratings from app download platforms and coded apps for evidence-based treatment components. We calculated Pearson correlation coefficients to identify associations between MARS scores, App Store iOS/Google Play consumer ratings, and number of evidence-based treatment components. RESULTS The mean MARS score was 3.52 (SD 0.71), consumer rating was 4.22 (SD 0.54), and number of evidence-based treatment components was 2.32 (SD 1.42). Consumer ratings were significantly correlated with the MARS Functionality subscale (r=0.74, <i>P</i>&lt;.001), Aesthetics subscale (r=0.70, <i>P</i>&lt;.01), and total score (r=0.58, <i>P</i>=.01). Number of evidence-based intervention components was not associated with MARS scores (r=0.085, <i>P</i>=.73) or consumer ratings (r=–0.329, <i>P</i>=.16). CONCLUSIONS In our analysis of popular research-supported consumer apps, objective app quality and subjective consumer ratings were generally high. App functionality and aesthetics were highly consistent with consumer appeal, whereas evidence-based components were not. In addition to designing treatments that work, we recommend that researchers prioritize aspects of app design that impact the user experience for engagement and sustainability (eg, ease of use, navigation, visual appeal). This will help translate evidence-based interventions to the competitive consumer app market, thus bridging the gap between research development and real-world implementation.

Population administration service satisfaction in Lima Puluh Kota District, West Sumatera

Assessment of public satisfaction with an agency's performance that organizes public services needs to be assessed by involving all service attributes because service attributes cannot stand alone, and performance improvement cannot be made separately. Assessment of service attributes is one way to track consumer ratings so that service providers can understand the causes of service problems. This study uses the Importance Performance Analysis (IPA) method. From this study, it was found that the priority that needs to be improved to improve public satisfaction with population administration services is to increase the discipline of officers in serving the community, certainty of service costs, and speed of service.

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Background Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. Objective This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. Methods Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. Results Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. Conclusions There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.