Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future.

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The day-today variations of sleep and physical activity are associated with various health outcomes in adults, and previous studies suggested a bidirectional association between these behaviors. The daily associations between sleep and physical activity have been examined in observational or interventional contexts. The primary goal of the current systematic review and meta-analysis was to summarize existing evidence about daily associations between sleep and physical activity outcomes at inter-and intraindividual level in adults. A systematic search of records in eight databases from inception to July 2019 identified 33 peer-reviewed empirical publications that examined daily sleep-physical activity association in adults. The qualitative and quantitative analyses of included studies did not support a bidirectional daily association between sleep outcomes and physical activity. Multilevel meta-analyses showed that three sleep parameters were associated with physical activity the following day: sleep quality, sleep efficiency, and wake after sleep onset. However, the associations were small, and varied in terms of direction and level of variability (e.g. inter-or intra-individual). Daytime physical activity was associated with lower total sleep time the following night at an inter-person level with a small effect size. Future studies should examine sleep and physical activity during a longer period and perform additional sophisticated statistical analyses.

Obesity can be prevented by the combined adoption of a regular physical activity (PA) and healthy eating behaviors (EB). Researchers mainly focused on socio-cognitive models, such as the Theory of Planned Behavior (TPB), to identify the psychological antecedents of these behaviors. However, few studies were interested in testing the potential contribution of automatic processes in the prediction of PA and EB. Thus, the main objective of this study was to explore the specific role of implicit attitudes in the pattern of prediction of self-reported PA and EB in the TPB framework, among persons with obesity and in adults from the general population. One hundred and fifty-three adults participated to this cross-sectional study among which 59 obese persons (74% women, age: 50.6 ± 12.3 years, BMI: 36.8 ± 4.03 kg m²) and 94 people from the general population (51% women; age: 34.7 ± 8.9 years). Implicit attitudes toward PA and EB were estimated through two Implicit Association Tests. TPB variables, PA and EB were assessed by questionnaire. Regarding to the prediction of PA, a significant contribution of implicit attitudes emerged in obese people, β = .25; 95%[CI: .01, .50]; P = .044, beyond the TPB variables, contrary to participants from the general population. The present study suggests that implicit attitudes play a specific role among persons with obesity regarding PA. Other studies are needed to examine which kind of psychological processes are specifically associated with PA and EB among obese people.

Within these models, explicit processes are described as less efficient and more intentional, controllable and consciously regulated than implicit processes (Bargh, 1994). These processes refer to facets of social-cognitive theories such as beliefs, expectations, intentions and the self-regulation of intention implementation (Rhodes, 2017). Implicit processes, on the contrary, are considered relatively more automatic (Bargh, 1994), such that their behavioral influences are presented as being more efficient, unintentional, uncontrollable, and less conscious than explicit processes (for a critical view of the distinction proposed here see:

This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. Methods 59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory-and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. Results Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs.

Therapy combined with physical exercise for depression, anxiety, fatigue and pain in adults with chronic diseases: systematic review and meta-analysis. Health Psychology (accepted) 1 Declaration of interestAll authors declare that they have no competing interests for this work.