Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

In the US, cardiovascular disease (CVD) is the leading cause of death in women¹ and accounts for approximately 1 in every 5 deaths among women.² Although there have been marked decreases in CVD mortality among both men and women, data suggest slowing of these decreases in CVD incidence and mortality among women.³ Social isolation and loneliness are prevalent psychosocial processes among adults⁴ and have been shown to be associated with increased risk of CVD among older adults.⁵ More than one-fourth of adults aged 65 or older are socially isolated, and one-third of adults aged 45 or older report being lonely.⁶ Of note, there is increasing evidence that both social isolation and loneliness are associated with CVD risk factors such as increased blood pressure,⁷ higher cholesterol levels, obesity, smoking,^8,9 physical inactivity,^10,11 and poor diet quality.¹² Whereas social isolation is the objective measure of social interactions and relationships, loneliness is the subjective feeling of being socially isolated.^6,13 The experiences of social isolation and feelings of loneliness have been shown to be mildly correlated¹⁴; however, they are distinct constructs, and research has shown that social isolation and loneliness are associated with different adverse outcomes of health and well-being.^14-17

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. 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. 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.

Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. Results: A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of −2.99 beats per minute (k comparison=74), −2.77 kcal per minute (k comparison=29), and −3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: −23.99 to 18.01, −12.75 to 7.41, and −13.07 to 6.86, respectively). Conclusions: Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

Exploring whether the mechanisms underlying the positive relationship between group exercise and physical activity are forms of social support – emotional, validation, informational, instrumental, and companionship and exercise identity. Participants (n = 506; M age = 34.3) completed a 235-item questionnaire assessing physical activity, exercise identity, social support, and other determinants of physical activity. Exploratory path analysis was used to model group exercise membership, forms of social support, exercise identity, and metabolic equivalent (MET) minutes/wk. Women and men had similar yet varying results. For women, group exercise membership was significantly associated with MET-minutes/wk (β = 0.11) and exercise identity (β = 0.17). There was a significant association between exercise identity and MET-minutes/wk (β = 0.38). Women perceived belonging to an exercise group provides emotional (β = 0.36), validation (β = 0.25), informational (β = 0.35), instrumental (β = 0.19), and companionship (β = 0.46) support. Validation (β = 0.11), informational (β = 0.21), and companionship (B = 0.17) were significantly associated with exercise identity for women. For men, group exercise membership was not significantly associated with MET-minutes/wk or exercise identity. Exercise identity was significantly associated with MET-minutes/wk (β = 0.46). Men perceived belonging to their group provides emotional (β = 0.31), validation (β = 0.32), informational (β = 0.33), and companionship (β = 0.34). Validation (β = 0.22), informational (β = 0.30), and emotional (β = 0.23) were significantly associated with exercise identity for men. Belonging to an exercise group is associated with forms of social support that strengthen exercise identity.