This systematic review aimed to determine which interventions increase physical activity (PA) and decrease sedentary behavior (SB) based on objective measures of movement behavior in individuals with stroke.
The PubMed (Medline), EMBASE, Scopus, CINAHL (EBSCO), and Web of Science databases were searched for articles published up to January 3, 2023.
The StArt 3.0.3 BETA software was used to screen titles, abstracts, and full texts for studies with randomized controlled trial designs; individuals with stroke (≥18 years of age); interventions aimed at increasing PA or decreasing SB; and objective measurement instruments.
Data extraction was standardized, considering participants and assessments of interest. The risk of bias and quality of evidence of the included studies were assessed.
Twenty-eight studies involving 1855 patients were included. Meta-analyses revealed that in the post-stroke acute/subacute phase, exercise interventions combined with behavior change techniques (BCTs) increased both daily steps (standardized mean difference [SMD]=0.65, P=.0002) and time spent on moderate-to-vigorous intensity physical activities (MVPAs) duration of PA (SMD=0.68, P=.0004) with moderate-quality evidence. In addition, interventions based only on BCTs increased PA levels with very low-quality evidence (SMD (low-intensity physical activity)=0.36, P=.02; SMD (MVPA)=0.56, P=.0004) and decreased SB with low-quality evidence (SMD=0.48, P=.03). In the post-stroke chronic phase, there is statistical significance in favor of exercise-only interventions in PA frequency (steps/day) with moderate-quality evidence (SMD=0.68, P=.002). In general, the risk of bias in the included studies was low.
In the acute/subacute phase after stroke, the use of BCTs combined with exercise can increase the number of daily steps and time spent on MVPA. In contrast, in the post-stroke chronic phase, exercise-only interventions resulted in a significant increase in daily steps.

Mobile health (mHealth) technology has assumed a pervasive role in healthcare and society. By capturing real-time features related to spine health, mHealth assessments have the potential to transform multiple aspects of spine care. Yet mHealth applications may not be familiar to many spine surgeons and other spine clinicians. Consequently, the objective of this narrative review is to provide an overview of the technology, analytical considerations, and applications of mHealth tools for evaluating spine surgery patients. Reflecting their near-ubiquitous role in society, smartphones are the most commonly available form of mHealth technology and can provide measures related to activity, sleep, and even social interaction. By comparison, wearable devices can provide more detailed mobility and physiological measures, although capabilities vary substantially by device. To date, mHealth evaluations in spine surgery patients have focused on the use of activity measures, particularly step counts, in an attempt to objectively quantify spine health. However, the correlation between step counts and patient-reported disease severity is inconsistent, and further work is needed to define the mobility metrics most relevant to spine surgery patients. mHealth assessments may also support a variety of other applications that have been studied less frequently, including those that prevent postoperative complications, predict surgical outcomes, and serve as motivational aids to patients. These areas represent key opportunities for future investigations. To maximize the potential of mHealth evaluations, several barriers must be overcome, including technical challenges, privacy and regulatory concerns, and questions related to reimbursement. Despite those obstacles, mHealth technology has the potential to transform many aspects of spine surgery research and practice, and its applications will only continue to grow in the years ahead.

Alzheimer\'s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer\'s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer\'s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer\'s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer\'s disease.

UNASSIGNED: To identify interventions using wearable accelerometers to measure physical activity and/or sedentary behaviour in adults during hospitalization for an acute medical/surgical condition.
UNASSIGNED: Four databases were searched in August 2019 (MEDLINE, CINAHL, Scopus, EMBASE).
UNASSIGNED: Studies were selected if they described an intervention in adults with a medical/surgical condition, and concurrently reported an accelerometer-derived measure of physical activity and/or sedentary behaviour while participants were admitted. Items were screened for eligibility in duplicate. Included studies were synthesized to describe intervention types, feasibility and potential effectiveness.
UNASSIGNED: Twenty-two studies were included, reporting on 3357 participants (2040 with accelerometer data). Identified types of interventions were: pre-habilitation (n = 2) exercise (n = 3), patient behaviour change with self-monitoring (n = 6), models of care (n = 5), implementing system change (n = 2), surgical technique (n = 2) patients wearing day clothes (n = 1) and education about activity in hospital (n = 1). Of 16 studies that reported intervention effects on physical activity, 11 reported a favourable impact including studies of: pre-habilitation, self-monitoring (accelerometry or an activity whiteboard), physiotherapy, an early mobility bundle, minimally invasive surgery, an education booklet and by implementing system change. Of the six studies that reported intervention effects on sedentary behaviour, there was a favourable impact with an activity whiteboard, models of care and an education booklet.
UNASSIGNED: Accelerometer-derived measures of physical activity and/or sedentary behaviour have been used to describe sample characteristics and intervention effects in studies of hospitalized adults. Interventions may involve a range of health professionals, but less is known about sedentary behaviour in this setting.

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing.

Efficient detection of estrus is a permanent challenge for successful reproductive performance in dairy cattle. In this context, comprehensive knowledge of estrus-related behaviors is fundamental to achieve optimal estrus detection rates. This review was designed to identify the characteristics of behavioral estrus as a necessary basis for developing strategies and technologies to improve the reproductive management on dairy farms. The focus is on secondary symptoms of estrus (mounting, activity, aggressive and agonistic behaviors) which seem more indicative than standing behavior. The consequences of management, housing conditions and cow- and environmental-related factors impacting expression and detection of estrus as well as their relative importance are described in order to increase efficiency and accuracy of estrus detection. As traditional estrus detection via visual observation is time-consuming and ineffective, there has been a considerable advancement of detection aids during the last 10 years. By now, a number of fully automated technologies including pressure sensing systems, activity meters, video cameras, recordings of vocalization as well as measurements of body temperature and milk progesterone concentration are available. These systems differ in many aspects regarding sustainability and efficiency as keys to their adoption for farm use. As being most practical for estrus detection a high priority - according to the current research - is given to the detection based on sensor-supported activity monitoring, especially accelerometer systems. Due to differences in individual intensity and duration of estrus multivariate analysis can support herd managers in determining the onset of estrus. Actually, there is increasing interest in investigating the potential of combining data of activity monitoring and information of several other methods, which may lead to the best results concerning sensitivity and specificity of detection. Future improvements will likely require more multivariate detection by data and systems already existing on farms.