Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios

Background Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. Objective The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. Methods First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. Results To minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. Conclusions In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.

Density-independent mortality at early life stages increases the probability of overlooking an underlying stock–recruitment relationship

Beverton and Holt’s (1957. On the dynamics of exploited fish populations. UK Ministry of Agriculture, Fisheries and Food. Fisheries Investigations, 2: 533 pp.) monograph contributed a widely used stock–recruitment relationship (BH-SRR) to fisheries science. However, because of variation around a presumed relationship between spawning biomass and recruits, the BH-SRR is often considered inadequate and approached merely as a curve-fitting exercise. The commonly used and simplified version of the BH-SRR has eclipsed the fact that in their classic monograph, the derivation accounted for mechanistic recruitment processes, including multi-stage recruitment with explicit cohort-dependent and -independent mortality terms that represent competition between recruits and extrinsic, cohort-independent factors such as the environment or predation as two independent sources of mortality. The original BH-SRR allows one to recreate recruitment patterns that correspond to observed ones. Doing so shows that variation in density-independent mortality increases the probability of overlooking an underlying stock–recruitment relationship. Intermediate coefficients of variation in mortality (75–100%) are sufficient to mask stock–recruitment relationships and recreate recruitment time series most similar to empirical data. This underlines the importance of variation in survival for recruitment and that Beverton and Holt’s work still provides a fundamental and useful tool to model the dynamics of populations.

Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios (Preprint)

BACKGROUND Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. OBJECTIVE The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. METHODS First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. RESULTS To minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. CONCLUSIONS In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.

How Do Older Adults Recruited Using MTurk Differ From Those in a National Probability Sample?

A growing number of studies within the field of gerontology have included samples recruited from Amazon’s Mechanical Turk (MTurk), an online crowdsourcing portal. While some research has examined how younger adult participants recruited through other means may differ from those recruited using MTurk, little work has addressed this question with older adults specifically. In the present study, we examined how older adults recruited via MTurk might differ from those recruited via a national probability sample, the Health and Retirement Study (HRS), on a battery of outcomes related to health and cognition. Using a Latin-square design, we examined the relationship between recruitment time, remuneration amount, and measures of cognitive functioning. We found substantial differences between our MTurk sample and the participants within the HRS, most notably within measures of verbal fluency and analogical reasoning. Additionally, remuneration amount was related to differences in time to complete recruitment, particularly at the lowest remuneration level, where recruitment completion required between 138 and 485 additional hours. While the general consensus has been that MTurk samples are a reasonable proxy for the larger population, this work suggests that researchers should be wary of overgeneralizing research conducted with older adults recruited through this portal.

Population Structure and Life Table of Dog Conch (Strombus turturella) in Bangka Belitung Islands, Indonesia [Struktur Populasi dan Tabel Hidup Siput Gonggong (Strombus turturella) di Kepulauan Bangka Belitung, Indonesia]

Dog Conch (Strombus turturella) has an essential economic value in Bangka Belitung Islands. Allegedly, the population of Dog Conch is decreasing due to overexploitation. The purpose of this study is to provide information related to the distribution of long frequency, growth pattern, age group, recruitment time estimation and life table of Dog Conch. This research took place on the coast of Tukak Village and Anak Air Island, Bangka Belitung Islands. Samples of Dog Conch were taken using 3x3 m2 square. The shell length of Dog Conch found ranged between 18.18 to 77.49 mm, consisting of three age groups. Asymptotic length value (L∞), growth coefficient (K) and theoretical age on zero-length (t0) were 83.94 mm, 0.79/year and -0.152 sequentially. In the first year, Dog Conch grows to 50.18 mm and slows down when it grows older until it is 13 years old. The proportion of high mortality rate was at 1 to 2 years old and 3 to 4 years old or in adult individuals, while the highest life expectancy rate was in the age group of 0-1-year old or young individuals. It indicated that the high mortality rate was in the group in which people use to consume or sell in the marketsAbstrakSiput gonggong (Strombus turturella) memiliki nilai ekonomis penting di Kepulauan Bangka Belitung. Diduga populasi siput gonggong semakin menurun akibat dari eksploitasi berlebihan. Tujuan penelitian ini adalah untuk memberikan informasi terkait distribusi frekuensi panjang, pola pertumbuhan, kelompok umur, estimasi waktu rekruitmen dan tabel hidup siput gonggong. Lokasi penelitian berada di Pesisir Desa Tukak dan Pulau Anak Air, Kepulauan Bangka Belitung.Pengambilan sampel siput gonggong dilakukan dengan menggunakan kuadrat 3x3 m2. Panjang cangkang siput gonggong yang ditemukan berkisar antara 18.18 s.d 77.49 mm yang terdiri atas 3 kelompok umur. Nilai panjang asymptotic (L∞), koefisien pertumbuhan (K) dan umur teoritis ketika panjang sama dengan nol (t0) adalah 83.94 mm, 0.79/tahun dan -0.152 secara berurutan. Pada tahun pertama siput gonggong mengalami pertumbuhan, mencapai 50.18 mm dan melambat ketika umur semakin tua hingga umur 13 tahun. Proporsi laju kematian tinggi terdapat pada umur 1 s.d 2 tahun dan 3 s.d 4 tahun atau pada individu dewasa, sedangkan nilai harapan hidup tertinggi terdapat pada kelompok umur 0-1 tahun atau individu muda. Hal ini menunjukkan bahwa kematian tertinggi terdapat pada kelompok umur yang telah diambil oleh masyarakat untuk dikonsumsi dan dijual ke pasaran.

Biomarker-based patient selection improves stroke rehabilitation trial efficiency

Background and PurposeInter-subject variability complicates trials of novel stroke rehabilitation therapies, particularly in the sub-acute phase after stroke. We tested whether selecting patients using motor evoked potential (MEP) status, a physiological biomarker of motor system function, could improve trial efficiency.MethodsA retrospective analysis of data from 207 patients (103 women, mean (SD) 70.6 (15.1) years) was used to estimate sample sizes and recruitment rates required to detect a 7-point difference between hypothetical control and treatment groups in upper-limb Fugl-Meyer and Action Research Arm Test scores at 90 days post-stroke. Analyses were carried out for the full sample and for subsets defined by motor evoked potential (MEP) status.ResultsSelecting patients according to MEP status reduced the required sample size by 75% compared to an unselected sample. The estimated time needed to recruit the required sample was also reduced by 72% for patients with MEPs, and was increased by 2-3-fold for patients without MEPs.ConclusionsUsing biomarkers to select patients can improve stroke rehabilitation trial efficiency by reducing the sample size and recruitment time needed to detect a clinically meaningful effect of the tested intervention.

Friends or Strangers? A Feasibility Study of an Innovative Focus Group Methodology

Focus groups are useful tools for examining perceptions, feelings, and suggestions about topics, products, or issues. Typically, focus groups are held in formal facilities with “strangers” or participants who do not know each other. Recent work suggests that “friendship groups” may provide an innovative alternative for collecting group-level qualitative data. This approach involves recruiting a single “source participant” who hosts a group in his/her home and recruits friends possessing the characteristics desired for the study. In order to examine the feasibility of friendship groups as a defensible research methodology, we conducted a series of four friendship groups as a feasibility study. Our analysis examined data from questionnaires about demographics, levels of acquaintanceship, and experience taking part in the group; transcripts; observational data; and the time and costs for recruiting. Using these data, we examined group dynamics, implementation issues, and recruitment time and costs. Based on these analyses, our study determined that friendship groups have the potential to be a viable and cost-effective method of qualitative inquiry.