Postponing old age: Evidence for historical change toward a later perceived onset of old age.

"At what age would you describe someone as old?" Perceptions of when old age begins might be prone to upward shifts because of historical increases in life expectancy and in retirement age, as well as because of better psychosocial functioning in later life. We investigated historical changes in within-person trajectories of the perceived onset of old age using data from 14,056 participants who entered the German Ageing Survey at age 40-85 years and who completed up to eight assessments across 25 years. Using longitudinal multilevel regression models, we found that at age 64, the average perceived onset of old age is at about age 75 years. Longitudinally, this perceived onset age increased by about 1 year for every 4-5 years of actual aging. We also found evidence for historical change. Compared to the earliest-born cohorts, later-born cohorts reported a later perceived onset of old age, yet with decelerating trend among more recent birth cohorts. Within-person increases of the perceived onset of old age were steeper in later-born cohorts. The described cohort trends were only slightly reduced when controlling for covariates. Being younger, male, living in East Germany, feeling older, reporting more loneliness, more chronic diseases, and poorer self-rated health were each associated with a perceived earlier onset of old age. Our results suggest that there is a nonlinear historical trend toward a later perceived onset of old age, which might have meaningful implications for individuals' perspectives on aging and old age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems.

BACKGROUND: When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems. METHODS: This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self-report assessment at ages 12, 14, and 16 or by a one-time retrospective self-report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self-reports, and when it was controlled for by retrospective self-reports. RESULTS: Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used. CONCLUSIONS: The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.

Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis.

BACKGROUND: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots. OBJECTIVE: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research. METHODS: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots). RESULTS: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness. CONCLUSIONS: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.

Variation in social media sensitivity across people and contexts.

Social media impacts people's wellbeing in different ways, but relatively little is known about why this is the case. Here we introduce the construct of "social media sensitivity" to understand how social media and wellbeing associations differ across people and the contexts in which these platforms are used. In a month-long large-scale intensive longitudinal study (total n = 1632; total number of observations = 120,599), we examined for whom and under which circumstances social media was associated with positive and negative changes in social and affective wellbeing. Applying a combination of frequentist and Bayesian multilevel models, we found a small negative average association between social media use AND subsequent wellbeing, but the associations were heterogenous across people. People with psychologically vulnerable dispositions (e.g., those who were depressed, lonely, not satisfied with life) tended to experience heightened negative social media sensitivity in comparison to people who were not psychologically vulnerable. People also experienced heightened negative social media sensitivity when in certain types of places (e.g., in social places, in nature) and while around certain types of people (e.g., around family members, close ties), as compared to using social media in other contexts. Our results suggest that an understanding of the effects of social media on wellbeing should account for the psychological dispositions of social media users, and the physical and social contexts surrounding their use. We discuss theoretical and practical implications of social media sensitivity for scholars, policymakers, and those in the technology industry.

Age-related emotional advantages in encountering novel situation in daily life.

People encounter novel situations throughout their lives that contribute to the acquisition of knowledge and experience. However, novelty can be misaligned with goals and motivation in later adulthood according to socioemotional selectivity theory. This study investigated age differences in emotional reactions associated with novel experiences. Multilevel structural equation models were used to analyze experience-sampling data obtained from an adult sample of 375 participants aged 18-94 years who reported their current situation and momentary emotional experience five times per day for 7 days. On occasions where situations were rated as more novel, people reported reduced positive and increased negative emotion. Those who had more overall exposure to novel situations tended to have more negative emotional experiences in general. Contrary to our hypothesis, there were age differences in individuals' negative emotional reactivity to situations that are perceived as more novel, such that novel situations were reported as less negative among older adults. By applying theoretical understanding of age differences in motivation and well-being in adulthood, our findings illuminate aspects of situations that elicit negative emotions. Findings highlight age-related benefits in emotional well-being, consistent with socioemotional selectivity theory postulates, and further implies that older adults may not be novelty averse. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stability and change of optimism and pessimism in late midlife and old age across three independent studies.

Research across a number of different areas in psychology has long shown that optimism and pessimism are predictive of a number of important future life outcomes. Despite a vast literature on the correlates and consequences, we know very little about how optimism and pessimism change across adulthood and old age and the sociodemographic factors that are associated with individual differences in such trajectories. In the present study, we conducted (parallel) analyses of standard items from the Life Orientation Test (Scheier & Carver, 1985) in three comprehensive data sets: Two-wave data from both the Berlin Aging Study II (N = 1,423, aged 60-88; M = 70.4, SD = 3.70) and the Midlife in the U.S. Study (N = 1,810 aged 60-84; M = 69.12, SD = 6.47) as well as cross-sectional data from the Survey of Health, Aging, and Retirement (N = 17,087, aged 60-99; M = 70.19, SD = 7.53). Using latent change-regression models and locally weighted smoothing curves revealed that optimism is on average very stable after age 60, with some evidence in Survey of Health, Aging, and Retirement of lowered optimism in very old age. Consistent across the three independent studies, pessimism evinced on average modest increases, ranging between .25 and .50 SD per 10 years of age. Of the sociodemographic factors examined, higher levels of education revealed the most consistent associations with lower pessimism, whereas gender evinced more study-specific findings. We take our results to demonstrate that age-related trajectories and correlates thereof differ for optimism and pessimism. Older adults appear to preserve into older ages those levels of optimistic expectations they have had at 60 years of age and show only modest increases in pessimism. We discuss possible reasons for these findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Promoting fluid intake to increase urine volume for kidney stone prevention: Protocol for a randomized controlled efficacy trial of the sipIT intervention.

BACKGROUND: Risk of kidney stone recurrence can be reduced by increasing fluid intake and urine production but most patients fail to adhere to recommended clinical guidelines. Patients have indicated that common barriers to fluid intake include a lack of thirst, forgetting to drink, and not having access to water. We developed the sipIT intervention to support patients' fluid intake with semi-automated tracking (via a mobile app, connected water bottle and a smartwatch clockface that detects drinking gestures) and provision of just-in-time text message reminders to drink when they do not meet the hourly fluid intake goal needed to achieve the recommended volume. This trial evaluates the efficacy of sipIT for increasing urine output in patients at risk for recurrence of kidney stones. METHOD/DESIGN: Adults with a history of kidney stones and lab-verified low urine production (<2 L/day) will be randomly assigned to receive either usual care (education and encouragement to meet fluid intake guidelines) or usual care plus the sipIT intervention. The primary outcome is 24-h urine volume; secondary outcomes include urinary supersaturations, past week fluid intake, and experienced automaticity of fluid intake. Outcomes will be assessed at baseline, 1 month, 3 months, and 12 months. CONCLUSIONS: The sipIT intervention is the first to prompt periodic fluid intake through integration of just-in-time notifications and semi-automated tracking. If sipIT is more efficacious than usual care, this intervention provides an innovative treatment option for patients needing support in meeting fluid intake guidelines for kidney stone prevention.

The effect of hair type and texture on electroencephalography and event-related potential data quality.

Research utilizing event-related potential (ERP) methods is generally biased with regard to sample representativeness. Among the myriad of factors that contribute to sample bias are researchers' assumptions about the extent to which racial differences in hair texture, volume, and style impact electrode placement, and subsequently, study eligibility. The current study examines these impacts using data collected from n = 213 individuals ages 17-19 years, and offers guidance on collection of ERP data across the full spectrum of hair types. Individual differences were quantified for hair texture using a visual scale, and for hair volume by measuring the amount of gel used in cap preparation. Electroencephalography data quality was assessed with multiple metrics at the preprocessing, post-processing, and variable generation stages. Results indicate that hair volume is associated with small, but systematic differences in signal quality and signal amplitude. Such differences are highly problematic as they could be misattributed to cognitive differences among groups. However, inclusion of gel volume as a covariate to account for individual differences in hair volume significantly reduced, and in most cases eliminated, group differences. We discuss strategies for overcoming real and perceived technical barriers for researchers seeking to achieve greater inclusivity and representativeness in ERP research.

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents.

Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Agemean 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

Studying Daily Social Interaction Quantity and Quality in Relation to Depression Change: A Multi-Phase Experience Sampling Study.

Day-to-day social life and mental health are intertwined. Yet, no study to date has assessed how the quantity and quality of social interactions in daily life are associated with changes in depressive symptoms. This study examines these links using multiple-timescale data (iSHAIB data set; N = 133), where the level of depressive symptoms was measured before and after three 21-day periods of event-contingent experience sampling of individuals' interpersonal interactions (T = 64,112). We find weak between-person effects for interaction quantity and perceiving interpersonal warmth of others on changes in depressive symptoms over the 21-day period, but strong and robust evidence for overwarming-a novel construct representing the self-perceived difference between one's own and interaction partner's level of interpersonal warmth. The findings highlight the important role qualitative aspects of social interactions may play in the progression of individuals' depressive symptoms.

Balancing media selections over time: Emotional valence, informational content, and time intervals of use.

The sequencing of information in media can influence processing of content via mechanisms like framing, mood management, and emotion regulation. This study examined three kinds of media sequences on smartphones: (1) balancing positive and negative emotional content; (2) balancing emotional content with informational content; and (3) balancing time spent on and off the media device. Actual media use was measured in natural settings using the Screenomics framework which gathers screenshots from smartphones every 5 s when devices are on. Time-series analyses of 223,531 smartphone sessions recorded from 94 participants showed that emotionally positive content was more likely to follow negative content, and that emotionally negative content was more likely to follow positive content; emotional content was more likely to follow informational content, and informational content was more likely to follow emotional content; and longer smartphone sessions were more likely to follow longer periods of non-use.

Contamination in Observational Research on Child Maltreatment: A Conceptual and Empirical Review With Implications for Future Research.

Contamination is a methodological phenomenon occurring in child maltreatment research when individuals in an established comparison condition have, in reality, been exposed to maltreatment during childhood. The current paper: (1) provides a conceptual and methodological introduction to contamination in child maltreatment research, (2) reviews the empirical literature demonstrating that the presence of contamination biases causal estimates in both prospective and retrospective cohort studies of child maltreatment effects, (3) outlines a dual measurement strategy for how child maltreatment researchers can address contamination, and (4) describes modern statistical methods for generating causal estimates in child maltreatment research after contamination is controlled. Our goal is to introduce the issue of contamination to researchers examining the effects of child maltreatment in an effort to improve the precision and replication of causal estimates that ultimately inform scientific and clinical decision-making as well as public policy.

Epigenetic age acceleration as a biomarker for impaired cognitive abilities in adulthood following early life adversity and psychiatric disorders.

Background: Early life adversity and psychiatric disorders are associated with earlier declines in neurocognitive abilities during adulthood. These declines may be preceded by changes in biological aging, specifically epigenetic age acceleration, providing an opportunity to uncover genome-wide biomarkers that identify individuals most likely to benefit from early screening and prevention. Methods: Five unique epigenetic age acceleration clocks derived from peripheral blood were examined in relation to latent variables of general and speeded cognitive abilities across two independent cohorts: 1) the Female Growth and Development Study (FGDS; n = 86), a 30-year prospective cohort study of substantiated child sexual abuse and non-abused controls, and 2) the Biological Classification of Mental Disorders study (BeCOME; n = 313), an adult community cohort established based on psychiatric disorders. Results: A faster pace of biological aging (DunedinPoAm) was associated with lower general cognitive abilities in both cohorts and slower speeded abilities in the BeCOME cohort. Acceleration in the Horvath clock was significantly associated with slower speeded abilities in the BeCOME cohort but not the FGDS. Acceleration in the Hannum clock and the GrimAge clock were not significantly associated with either cognitive ability. Accelerated PhenoAge was associated with slower speeded abilities in the FGDS but not the BeCOME cohort. Conclusions: The present results suggest that epigenetic age acceleration has the potential to serve as a biomarker for neurocognitive decline in adults with a history of early life adversity or psychiatric disorders. Estimates of epigenetic aging may identify adults at risk of cognitive decline that could benefit from early neurocognitive screening.

Long-term aging trajectories of the accumulation of disease burden as predictors of daily affect dynamics and stressor reactivity.

Multiple-timescale studies provide new opportunities to examine how developmental processes that evolve at different cadences are intertwined. Developmental theories of emotion regulation suggest that the long-term, slowly evolving age-related accumulation of disease burden should shape short-term, faster evolving (daily) affective experiences. To empirically examine this proposition, we combined data from 123 old adults (65-69 years, 47% women) and 32 very old adults (85-88 years, 59% women) who provided 20 + year within-person longitudinal data on physician-rated morbidity and subsequently also completed repeated daily-life assessments of stress and affect six times a day over 7 consecutive days as they were going about their daily-life routines. Results from models that simultaneously articulate growth and intraindividual variability processes (in a dynamic structural equation modeling framework) revealed that individual differences in long-term aging trajectories of the accumulation of disease burden were indeed predictive of differences in three facets of affective dynamics that manifest in everyday life. In particular-over and above mean levels of disease burden-older adults whose disease burden had increased more over the past 20 years had higher base level of negative affect in their daily lives, more emotional reactivity to the experience of daily stressors, and more moment-to-moment fluctuations in negative affect that was unrelated to stressors (affective systemic noise). We highlight that developmental processes evolving over vastly different timescales are intertwined, and speculate how new knowledge about those relations can inform developmental theories of emotion regulation and daily-life functioning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods.

The depth of information collected in participants' daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When measuring participants' behaviors in daily life, the timing of particular events-such as social interactions-is often recorded. These data facilitate the investigation of new types of research questions about the timing of those events, including whether individuals' affective state is associated with the rate of social interactions (binary event occurrence) and what types of social interactions are likely to occur (multicategory event occurrences, e.g., interactions with friends or family). Although survival analysis methods have been used to analyze time-to-event data in longitudinal settings for several decades, these methods have not yet been incorporated into ambulatory assessment research. This article illustrates how multilevel and multistate survival analysis methods can be used to model the social interaction dynamics captured in intensive longitudinal data, specifically when individuals exhibit particular categories of behavior. We provide an introduction to these models and a tutorial on how the timing and type of social interactions can be modeled using the R statistical programming language. Using event-contingent reports (N = 150, Nevents = 64,112) obtained in an ambulatory study of interpersonal interactions, we further exemplify an empirical application case. In sum, this article demonstrates how survival models can advance the understanding of (social interaction) dynamics that unfold in daily life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modeling BMI z score lability during childhood as a function of child temperament and genetic risk for obesity.

OBJECTIVE: This study aimed to examine whether child genetic risk for obesity and temperament (i.e., negative affectivity, effortful control) accounted for stability versus lability in children's weight status (BMI z score) over time. METHODS: A total of 561 adopted children (42% female; 56% Caucasian, 13% African American, 11% Latino, and 20% other) and their birth and adoptive parents were followed from birth to age 9 years. The multilevel location-scale model was used to examine whether child genetic risk for obesity and temperament were related to differences in level and lability in child BMI z scores over time. RESULTS: For the full sample, higher levels of child negative affectivity were associated with greater BMI z score lability, whereas higher levels of effortful control and children's mean-level BMI z scores were related to less lability across childhood. Additional analyses examined associations within groups of children with healthy versus overweight/obesity weight statuses. Within the healthy weight status group only, better effortful control was associated with more stable BMI z scores, whereas genetic risk for higher BMI was associated with more labile BMI z scores. CONCLUSIONS: These findings provide insights into factors that can be harnessed to redirect unhealthy trajectories as well as factors that may challenge redirection or maintain a healthy trajectory.

The Affective Dynamics of Everyday Digital Life: Opening Computational Possibility.

Up to now, there was no way to observe and track the affective impacts of the massive amount of complex visual stimuli that people encounter "in the wild" during their many hours of digital life. In this paper, we propose and illustrate how recent advances in AI-trained ensembles of deep neural networks-can be deployed on new data streams that are long sequences of screenshots of study participants' smartphones obtained unobtrusively during everyday life. We obtained affective valence and arousal ratings of hundreds of images drawn from existing picture repositories often used in psychological studies, and a new screenshot repository chronicling individuals' everyday digital life from both N = 832 adults and an affect computation model (Parry & Vuong, 2021). Results and analysis suggest that (a) our sample rates images similarly to other samples used in psychological studies, (b) the affect computation model is able to assign valence and arousal ratings similarly to humans, and (c) the resulting computational pipeline can be deployed at scale to obtain detailed maps of the affective space individuals travel through on their smartphones. Leveraging innovative methods for tracking the emotional content individuals encounter on their smartphones, we open the possibility for large-scale studies of how the affective dynamics of everyday digital life shape individuals' moment-to-moment experiences and well-being. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00202-4.

Examining individual differences in how interaction behaviors change over time: A dyadic multinomial logistic growth modeling approach.

Several theoretical perspectives suggest that dyadic experiences are distinguished by patterns of behavioral change that emerge during interactions. Methods for examining change in behavior over time are well elaborated for the study of change along continuous dimensions. Extensions for charting increases and decreases in individuals' use of specific, categorically defined behaviors, however, are rarely invoked. Greater accessibility of Bayesian frameworks that facilitate formulation and estimation of the requisite models is opening new possibilities. This article provides a primer on how multinomial logistic growth models can be used to examine between-dyad differences in within-dyad behavioral change over the course of an interaction. We describe and illustrate how these models are implemented in the Bayesian framework using data from support conversations between strangers (N = 118 dyads) to examine (RQ1) how six types of listeners' and disclosers' behaviors change as support conversations unfold and (RQ2) how the disclosers' preconversation distress moderates the change in conversation behaviors. The primer concludes with a series of notes on (a) implications of modeling choices, (b) flexibility in modeling nonlinear change, (c) necessity for theory that specifies how and why change trajectories differ, and (d) how multinomial logistic growth models can help refine current theory about dyadic interaction. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Feasibility of Mini sipIT Behavioral Intervention to Increase Urine Volume in Patients With Kidney Stones.

OBJECTIVE: To determine the feasibility and acceptability of mini sipIT, a context-sensitive reminder system that incorporates a connected water bottle and mobile app with text messaging, for kidney stone patients who have poor adherence to increasing fluid intake for prevention. METHODS: Patients with a history of kidney stones and urine volume <2L/d participated in a 1-month single-group feasibility trial. Patients used a connected water bottle and received text message reminders when fluid intake goals weren't met. Perceptions of drinking behavior, intervention acceptability, and 24-hour urine volumes were obtained at baseline and 1-month. RESULTS: Patients with a history of kidney stones were enrolled (n = 26, 77% female, age=50.4 ±â€¯14.2years). Over 90% of patients used the bottle or app daily. Most patients perceived that mini sipIT intervention helped them to increase their fluid intake (85%) and reach their fluid intake goals (65%). There was a significant increase in average 24-hour urine volume after the 1-month intervention compared to baseline (2006.5 ±â€¯980.8 mL vs 1352.7 ±â€¯449.9 mL, t (25)= 3.66, P = .001, g= 0.78), with 73% of patients having higher 24-hour urine volumes at the end of the trial. CONCLUSION: Mini sipIT behavioral intervention and outcome assessments are feasible for patients and may lead to significant increases in 24-hour urine volume. Digital tools in combination with behavioral science may improve adherence to fluid intake recommendations for kidney stone prevention, however, rigorous efficacy trials are necessary.

Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability.

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about generalizability of results might be productively reframed with respect to transferability of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about generalizability commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of transferability."