LiveWell, a smartphone-based self-management intervention for bipolar disorder: Intervention participation and usability analysis.

BACKGROUND: Understanding how individuals utilize and perceive digital mental health interventions may improve engagement and effectiveness. To support intervention improvement, participant feedback was obtained and app use patterns were examined for a randomized clinical trial evaluating a smartphone-based intervention for individuals with bipolar disorder. METHODS: App use and coaching engagement were examined (n = 124). Feedback was obtained via exit questionnaires (week 16, n = 81) and exit interviews (week 48, n = 17). RESULTS: On average, over 48 weeks, participants used the app for 4.4 h and engaged with the coach for 3.9 h. Participants spent the most time monitoring target behaviors and receiving adaptive feedback and the least time viewing self-assessments and skills. Participants reported that the daily check in helped increase awareness of target behaviors but expressed frustration with repetitiveness of monitoring and feedback content. Participants liked personalizing their wellness plan, but its use did not facilitate skills practice. App use declined over time which participants attributed to clinical stability, content mastery, and time commitment. Participants found the coaching supportive and motivating for app use. LIMITATIONS: App engagement based on viewing time may overestimate engagement. The delay between intervention delivery and the exit interviews and low exit interview participation may introduce bias. CONCLUSION: Utilization patterns and feedback suggest that digital mental health engagement and efficacy may benefit from adaptive personalization of targets monitored combined with adaptive monitoring and feedback to support skills practice and development. Increasing engagement with supports may also be beneficial.

Effects of a Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder on Relapse, Symptom Burden, and Quality of Life: A Randomized Clinical Trial.

Importance: Bipolar disorder-specific psychotherapy combined with pharmacotherapy improves relapse risk, symptom burden, and quality of life, but psychotherapy is not easily accessible. Objective: To determine if a smartphone-based self-management intervention (LiveWell) can assist individuals with bipolar disorder to maintain wellness. Design, Setting, and Participants: An assessor-blind randomized clinical trial enrolled participants from March 20, 2017, to April 25, 2019, with 48-week follow-up ending on April 10, 2020. Participants were randomly assigned to usual care or usual care plus the smartphone intervention stratified by relapse risk based on initial clinical status (low risk: asymptomatic recovery; high risk: continued symptomatic, prodromal, recovering, symptomatic recovery). Participants with bipolar disorder I were recruited from clinics in the Chicago and Minneapolis-Saint Paul areas. Data were analyzed from June 19, 2020, to May 25, 2022. Interventions: The smartphone-based self-management intervention consisted of an application (app), coach, and website. Over 16 weeks, participants had a coach visit followed by 6 phone calls, and they completed daily and weekly app check-ins. The app provided adaptive feedback and information for developing a personalized wellness plan, the coach provided support, and the website provided summary data and alerts. Main Outcomes and Measures: The primary outcome was time to relapse. Secondary outcomes were percentage-time symptomatic, symptom severity, and quality of life. Results: Of the 205 randomized participants (mean [SD] age, 42 [12] years; 125 female individuals [61%]; 5 Asian [2%], 21 Black [10%], 13 Hispanic or Latino [6%], 7 multiracial [3%], 170 White [83%], 2 unknown race [1%]), 81 (40%) were randomly assigned to usual care, and 124 (60%) were randomly assigned to usual care plus the smartphone intervention. This clinical trial did not detect a reduction in relapse risk for the smartphone intervention (hazard ratio [HR], 0.65; 95% CI, 0.39-1.09; log-rank P = .08). However, decreased relapse was observed for low-risk individuals (HR, 0.32; 95% CI, 0.12-0.88; log-rank P = .02) but not high-risk individuals (HR, 0.86; 95% CI, 0.47-1.57; log-rank P = .62). Reduced manic symptom severity was observed for low-risk individuals (mean [SE] difference, -1.4 [0.4]; P = .001) but not for high-risk individuals (mean [SE] difference, 0 [0.3]; P = .95). The smartphone-based self-management intervention decreased depressive symptom severity (mean [SE] difference, -0.80 [0.34]; P = .02) and improved relational quality of life (mean [SE] difference, 1.03 [0.45]; P = .02) but did not decrease percentage-time symptomatic (mean [SE] difference, -5.6 [4.3]; P = .20). Conclusions and Relevance: This randomized clinical trial of a smartphone-based self-management intervention did not detect a significant improvement in the primary outcome of time to relapse. However, a significant decrease in relapse risk was observed for individuals in asymptomatic recovery. In addition, the intervention decreased depressive symptom severity and improved relational quality of life. These findings warrant further work to optimize the smartphone intervention and confirm that the intervention decreases relapse risk for individuals in asymptomatic recovery. Trial Registration: ClinicalTrials.gov Identifier: NCT03088462.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial.

BACKGROUND: Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment. OBJECTIVE: A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response. METHODS: The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk. RESULTS: Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022. CONCLUSIONS: This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30710.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback.

BACKGROUND: Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions. OBJECTIVE: To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder. METHODS: Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system. RESULTS: Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers. CONCLUSIONS: Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.

A Smartphone-Based Self-Management Intervention for Individuals with Bipolar Disorder (LiveWell): Qualitative Study on User Experiences of the Behavior Change Process.

BACKGROUND: Bipolar disorder is a severe mental illness characterized by recurrent episodes of depressed, elevated, and mixed mood states. The addition of psychotherapy to pharmacological management can decrease symptoms, lower relapse rates, and improve quality of life; however, access to psychotherapy is limited. Mental health technologies such as smartphone apps are being studied as a means to increase access to and enhance the effectiveness of adjunctive psychotherapies for bipolar disorder. Individuals with bipolar disorder find this intervention format acceptable, but our understanding of how people utilize and integrate these tools into their behavior change and maintenance processes remains limited. OBJECTIVE: The objective of this study was to explore how individuals with bipolar disorder perceive and utilize a smartphone intervention for health behavior change and maintenance. METHODS: Individuals with bipolar disorder were recruited via flyers placed at university-affiliated and private outpatient mental health practices to participate in a pilot study of LiveWell, a smartphone-based self-management intervention. At the end of the study, all participants completed in-depth qualitative exit interviews. The behavior change framework developed to organize the intervention design was used to deductively code behavioral targets and determinants involved in target engagement. Inductive coding was used to identify themes not captured by this framework. RESULTS: In terms of behavioral targets, participants emphasized the importance of managing mood episode-related signs and symptoms. They also discussed the importance of maintaining regular routines, sleep duration, and medication adherence. Participants emphasized that receiving support from a coach as well as seeking and receiving assistance from family, friends, and providers were important for managing behavioral targets and staying well. In terms of determinants, participants stressed the important role of monitoring for their behavior change and maintenance efforts. Monitoring facilitated self-awareness and reflection, which was considered valuable for staying well. Some participants also felt that the intervention facilitated learning information necessary for managing bipolar disorder but others felt that the information provided was too basic. CONCLUSIONS: In addition to addressing acceptability, satisfaction, and engagement, a person-based design of mental health technologies can be used to understand how people experience the impact of these technologies on their behavior change and maintenance efforts. This understanding may then be used to guide ongoing intervention development. The participants' perceptions aligned with the intervention's primary behavioral targets and use of a monitoring tool as a core intervention feature. Participant feedback further indicates that developing additional content and tools to address building and engaging social support may be an important avenue for improving LiveWell. A comprehensive behavior change framework to understand participant perceptions of their behavior change and maintenance efforts may help facilitate ongoing intervention development.

A Smartphone-Based Self-management Intervention for Bipolar Disorder (LiveWell): User-Centered Development Approach.

BACKGROUND: Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care. OBJECTIVE: This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions. METHODS: Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback. RESULTS: The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support. CONCLUSIONS: User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development.

Development of Coaching Support for LiveWell: A Smartphone-Based Self-Management Intervention for Bipolar Disorder.

Despite effective pharmacological treatment, bipolar disorder is a leading cause of disability due to recurrence of episodes, long episode durations, and persistence of interepisode symptoms. While adding psychotherapy to pharmacotherapy improves outcomes, the availability of adjunctive psychotherapy is limited. To extend the accessibility and functionality of psychotherapy for bipolar disorder, we developed LiveWell, a smartphone-based self-management intervention. Unfortunately, many mental health technology interventions suffer from high attrition rates, with users rapidly failing to maintain engagement with the intervention technology. Human support reduces this commonly observed engagement problem but does not consistently improve clinical and recovery outcomes. To facilitate ongoing efforts to develop human support for digital mental health technologies, this paper describes the design decisions, theoretical framework, content, mode, timing of delivery, and the training and supervision for coaching support of the LiveWell technology. This support includes clearly defined and structured roles that aim to encourage the use of the technology, self-management strategies, and communication with care providers. A clear division of labor is established between the coaching support roles and the intervention technology to allow lay personnel to serve as coaches and thereby maximize accessibility to the LiveWell intervention.

Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting.

BACKGROUND: Cellular mobile telephone technology shows much promise for delivering and evaluating healthcare interventions in cost-effective manners with minimal barriers to access. There is little data demonstrating that these devices can accurately measure clinically important aspects of individual functional status in naturalistic environments outside of the laboratory. OBJECTIVE: The objective of this study was to demonstrate that data derived from ubiquitous mobile phone technology, using algorithms developed and previously validated by our lab in a controlled setting, can be employed to continuously and noninvasively measure aspects of participant (subject) health status including step counts, gait speed, and activity level, in a naturalistic community setting. A second objective was to compare our mobile phone-based data against current standard survey-based gait instruments and clinical physical performance measures in order to determine whether they measured similar or independent constructs. METHODS: A total of 43 ambulatory, independently dwelling older adults were recruited from Nebraska Medicine, including 25 (58%, 25/43) healthy control individuals from our Engage Wellness Center and 18 (42%, 18/43) functionally impaired, cognitively intact individuals (who met at least 3 of 5 criteria for frailty) from our ambulatory Geriatrics Clinic. The following previously-validated surveys were obtained on study day 1: (1) Late Life Function and Disability Instrument (LLFDI); (2) Survey of Activities and Fear of Falling in the Elderly (SAFFE); (3) Patient Reported Outcomes Measurement Information System (PROMIS), short form version 1.0 Physical Function 10a (PROMIS-PF); and (4) PROMIS Global Health, short form version 1.1 (PROMIS-GH). In addition, clinical physical performance measurements of frailty (10 foot Get up and Go, 4 Meter walk, and Figure-of-8 Walk [F8W]) were also obtained. These metrics were compared to our mobile phone-based metrics collected from the participants in the community over a 24-hour period occurring within 1 week of the initial assessment. RESULTS: We identified statistically significant differences between functionally intact and frail participants in mobile phone-derived measures of percent activity (P=.002, t test), active versus inactive status (P=.02, t test), average step counts (P<.001, repeated measures analysis of variance [ANOVA]) and gait speed (P<.001, t test). In functionally intact individuals, the above mobile phone metrics assessed aspects of functional status independent (Bland-Altman and correlation analysis) of both survey- and/or performance battery-based functional measures. In contrast, in frail individuals, the above mobile phone metrics correlated with submeasures of both SAFFE and PROMIS-GH. CONCLUSIONS: Continuous mobile phone-based measures of participant community activity and mobility strongly differentiate between persons with intact functional status and persons with a frailty phenotype. These measures assess dimensions of functional status independent of those measured using current validated questionnaires and physical performance assessments to identify functional compromise. Mobile phone-based gait measures may provide a more readily accessible and less-time consuming measure of gait, while further providing clinicians with longitudinal gait measures that are currently difficult to obtain.

Age-related changes in cerebellar and hypothalamic function accompany non-microglial immune gene expression, altered synapse organization, and excitatory amino acid neurotransmission deficits.

We describe age-related molecular and neuronal changes that disrupt mobility or energy balance based on brain region and genetic background. Compared to young mice, aged C57BL/6 mice exhibit marked locomotor (but not energy balance) impairments. In contrast, aged BALB mice exhibit marked energy balance (but not locomotor) impairments. Age-related changes in cerebellar or hypothalamic gene expression accompany these phenotypes. Aging evokes upregulation of immune pattern recognition receptors and cell adhesion molecules. However, these changes do not localize to microglia, the major CNS immunocyte. Consistent with a neuronal role, there is a marked age-related increase in excitatory synapses over the cerebellum and hypothalamus. Functional imaging of these regions is consistent with age-related synaptic impairments. These studies suggest that aging reactivates a developmental program employed during embryogenesis where immune molecules guide synapse formation and pruning. Renewed activity in this program may disrupt excitatory neurotransmission, causing significant behavioral deficits.

Assessing Smart Phones for Generating Life-space Indicators.

Life-space is a promising method for estimating older adults' functional status. However, traditional life-space measures are costly and time consuming because they often rely on active subject participation. This study assesses the feasibility of using the global positioning system (GPS) function of smart phones to generate life-space indicators. We first evaluated the location accuracy of smart phone collected GPS points versus those acquired by a commercial GPS unit. We then assessed the specificity of the smart phone processed life-space information against the traditional diary method. Our results suggested comparable location accuracy between the smart phone and the standard GPS unit in most outdoor situations. In addition, the smart phone method revealed more comprehensive life-space information than the diary method, which leads to higher and more consistent life-space scores. We conclude that the smart phone method is more reliable than traditional methods for measuring life-space. Further improvements will be required to develop a robust application of this method that is suitable for health-related practices.

Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers.

A number of important health-related outcomes are directly related to a person's ability to maintain normal gait speed. We hypothesize that cellular telephones may be repurposed to measure this important behavior in a noninvasive, continuous, precise, and inexpensive manner. The purpose of this study was to determine if physical activity (PA) counts collected by cell phone accelerometers could measure treadmill gait speeds. We also assessed how cell phone placement influenced treadmill gait speed measures. Participants included 55 young, middle-aged, and older community-dwelling men and women. We placed cell phones as a pendant around the neck, and on the left and right wrist, hip, and ankle. Subjects then completed an individualized treadmill protocol, alternating 1 min rest periods with 5 min of walking at different speeds (0.3-11.3 km/h; 0.2-7 mi/h). No persons were asked to walk at speeds faster than what they would achieve during day-to-day life. PA counts were calculated from all sensor locations. We built linear mixed statistical models of PA counts predicted by treadmill speeds ranging from 0.8 to 6.4 km/h (0.5-4 mi/h) while accounting for subject age, weight, and gender. We solved linear regression equations for treadmill gait speed, expressed as a function of PA counts, age, weight, and gender. At all locations, cell phone PA counts were strongly associated with treadmill gait speed. Cell phones worn at the hip yielded the best predictive model. We conclude that in both men and women, cell phone derived activity counts strongly correlate with treadmill gait speed over a wide range of subject ages and weights.

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment.

Inexpensive, high-throughput, low maintenance systems for precise temporal and spatial measurement of mouse home cage behavior (including movement, feeding, and drinking) are required to evaluate products from large scale pharmaceutical design and genetic lesion programs. These measurements are also required to interpret results from more focused behavioral assays. We describe the design and validation of a highly-scalable, reliable mouse home cage behavioral monitoring system modeled on a previously described, one-of-a-kind system. Mouse position was determined by solving static equilibrium equations describing the force and torques acting on the system strain gauges; feeding events were detected by a photobeam across the food hopper, and drinking events were detected by a capacitive lick sensor. Validation studies show excellent agreement between mouse position and drinking events measured by the system compared with video-based observation--a gold standard in neuroscience.

A flexible estimating equations approach for mapping function-valued traits.

In genetic studies, many interesting traits, including growth curves and skeletal shape, have temporal or spatial structure. They are better treated as curves or function-valued traits. Identification of genetic loci contributing to such traits is facilitated by specialized methods that explicitly address the function-valued nature of the data. Current methods for mapping function-valued traits are mostly likelihood-based, requiring specification of the distribution and error structure. However, such specification is difficult or impractical in many scenarios. We propose a general functional regression approach based on estimating equations that is robust to misspecification of the covariance structure. Estimation is based on a two-step least-squares algorithm, which is fast and applicable even when the number of time points exceeds the number of samples. It is also flexible due to a general linear functional model; changing the number of covariates does not necessitate a new set of formulas and programs. In addition, many meaningful extensions are straightforward. For example, we can accommodate incomplete genotype data, and the algorithm can be trivially parallelized. The framework is an attractive alternative to likelihood-based methods when the covariance structure of the data is not known. It provides a good compromise between model simplicity, statistical efficiency, and computational speed. We illustrate our method and its advantages using circadian mouse behavioral data.

Cellular telephones measure activity and lifespace in community-dwelling adults: proof of principle.

OBJECTIVES: To describe a system that uses off-the-shelf sensor and telecommunication technologies to continuously measure individual lifespace and activity levels in a novel way. DESIGN: Proof of concept involving three field trials of 30, 30, and 21 days. SETTING: Omaha, Nebraska, metropolitan and surrounding rural region. PARTICIPANTS: Three participants (48-year-old man, 33-year-old woman, and 27-year-old male), none with any functional limitations. MEASUREMENTS: Cellular telephones were used to detect in-home position and in-community location and to measure physical activity. Within the home, cellular telephones and Bluetooth transmitters (beacons) were used to locate participants at room-level resolution. Outside the home, the same cellular telephones and global positioning system (GPS) technology were used to locate participants at a community-level resolution. Physical activity was simultaneously measured using the cellular telephone accelerometer. RESULTS: This approach had face validity to measure activity and lifespace. More importantly, this system could measure the spatial and temporal organization of these metrics. For example, an individual's lifespace was automatically calculated across multiple time intervals. Behavioral time budgets showing how people allocate time to specific regions within the home were also automatically generated. CONCLUSION: Mobile monitoring shows much promise as an easily deployed system to quantify activity and lifespace, important indicators of function, in community-dwelling adults.

Behavioral indices of ongoing pain are largely unchanged in male mice with tissue or nerve injury-induced mechanical hypersensitivity.

Despite the impact of chronic pain on the quality of life in patients, including changes to affective state and daily life activities, rodent preclinical models rarely address this aspect of chronic pain. To better understand the behavioral consequences of the tissue and nerve injuries typically used to model neuropathic and inflammatory pain in mice, we measured home cage and affective state behaviors in animals with spared nerve injury, chronic constriction injury (CCI), or intraplantar complete Freund's adjuvant. Mechanical hypersensitivity is prominent in each of these conditions and persists for many weeks. Home cage behavior was continuously monitored for 16 days in a system that measures locomotion, feeding, and drinking, and allows for precise analysis of circadian patterns. When monitored after injury, animals with spared nerve injury and complete Freund's adjuvant behaved no differently from controls in any aspect of daily life. Animals with CCI were initially less active, but the difference between CCI and controls disappeared by 2 weeks after injury. Further, in all pain models, there was no change in any measure of affective state. We conclude that in these standard models of persistent pain, despite the development of prolonged hypersensitivity, the mice do not have significantly altered "quality of life." As alteration in daily life activities is the feature that is so disrupted in patients with chronic pain, our results suggest that the models used here do not fully reflect the human conditions and point to a need for development of a murine chronic pain model in which lifestyle changes are manifest.

A robust automated system elucidates mouse home cage behavioral structure.

Patterns of behavior exhibited by mice in their home cages reflect the function and interaction of numerous behavioral and physiological systems. Detailed assessment of these patterns thus has the potential to provide a powerful tool for understanding basic aspects of behavioral regulation and their perturbation by disease processes. However, the capacity to identify and examine these patterns in terms of their discrete levels of organization across diverse behaviors has been difficult to achieve and automate. Here, we describe an automated approach for the quantitative characterization of fundamental behavioral elements and their patterns in the freely behaving mouse. We demonstrate the utility of this approach by identifying unique features of home cage behavioral structure and changes in distinct levels of behavioral organization in mice with single gene mutations altering energy balance. The robust, automated, reproducible quantification of mouse home cage behavioral structure detailed here should have wide applicability for the study of mammalian physiology, behavior, and disease.

Deletion of Mecp2 in Sim1-expressing neurons reveals a critical role for MeCP2 in feeding behavior, aggression, and the response to stress.

Rett Syndrome (RTT) is an autism spectrum disorder caused by mutations in the X-linked gene encoding methyl-CpG binding protein 2 (MeCP2). In order to map the neuroanatomic origins of the complex neuropsychiatric behaviors observed in patients with RTT and to uncover endogenous functions of MeCP2 in the hypothalamus, we removed Mecp2 from Sim1-expressing neurons in the hypothalamus using Cre-loxP technology. Loss of MeCP2 in Sim1-expressing neurons resulted in mice that recapitulated the abnormal physiological stress response that is seen upon MeCP2 dysfunction in the entire brain. Surprisingly, we also uncovered a role for MeCP2 in the regulation of social and feeding behaviors since the Mecp2 conditional knockout (CKO) mice were aggressive, hyperphagic, and obese. This study demonstrates that deleting Mecp2 in a defined brain region is an excellent approach to map the neuronal origins of complex behaviors and provides new insight about the function of MeCP2 in specific neurons.

Synergistic impairment of glucose homeostasis in ob/ob mice lacking functional serotonin 2C receptors.

To investigate how serotonin and leptin interact in the regulation of energy balance and glucose homeostasis, we generated a genetic mouse model, the OB2C mouse, which lacks functional serotonin 2C receptors and the adipocyte hormone leptin. The OB2C mice exhibited a dramatic diabetes phenotype, evidenced by a synergistic increase in serum glucose levels and water intake. The severity of the animals' diabetes phenotype would not have been predicted from the phenotypic characterization of mice bearing mutations of either the leptin (OB mutant mice) or the serotonin 2C receptor gene (2C mutant mice). The synergistic impairment in glucose homeostasis developed at an age when OB2C mice did not differ in body weight from OB mice, suggesting that this impairment was not an indirect consequence of increased adiposity. We also demonstrated that the improvement in glucose tolerance in wild-type mice treated with the serotonin releaser and reuptake inhibitor fenfluramine was blunted in 2C mutant mice. These pharmacological and genetic findings provide evidence that the serotonin 2C receptor has direct effects on glucose homeostasis.

Brain-derived neurotrophic factor regulates energy balance downstream of melanocortin-4 receptor.

The melanocortin-4 receptor (MC4R) is critically involved in regulating energy balance, and obesity has been observed in mice with mutations in the gene for brain-derived neurotrophic factor (BDNF). Here we report that BDNF is expressed at high levels in the ventromedial hypothalamus (VMH) where its expression is regulated by nutritional state and by MC4R signaling. In addition, similar to MC4R mutants, mouse mutants that expresses the BDNF receptor TrkB at a quarter of the normal amount showed hyperphagia and excessive weight gain on higher-fat diets. Furthermore, BDNF infusion into the brain suppressed the hyperphagia and excessive weight gain observed on higher-fat diets in mice with deficient MC4R signaling. These results show that MC4R signaling controls BDNF expression in the VMH and support the hypothesis that BDNF is an important effector through which MC4R signaling controls energy balance.

Hyperactivity and reduced energy cost of physical activity in serotonin 5-HT(2C) receptor mutant mice.

We have observed late-onset obesity in mutant mice lacking the serotonin 5-HT(2C) receptor. Despite chronically elevated food intake, young adult mutants exhibit neither elevated adiposity nor altered glucose or fat homeostasis. However, obesity subsequently develops after 6 months of age without increases in their level of hyperphagia. In this study, we investigated determinants of energy expenditure in 5-HT(2C) receptor mutant mice. Young adult mutants displayed patterns of elevated activity levels that were enhanced by fasting and tightly associated with repeated visits to a food source. Surprisingly, subsequent obesity development occurred despite persisting locomotor hyperactivity and without age-related declines in resting metabolic rate. Rather, substantial reductions in the energy cost of locomotor activity (LA) were observed in 5-HT(2C) receptor mutant mice. Moreover, both mutant and wild-type mice displayed age-related declines in the energy cost of LA, indicating that this process may be regulated by both aging and serotonergic signaling. These results indicate that a mutation of the 5-HT(2C) receptor gene (htr2c) increases LA, which contributes to the maintenance of normal body composition in young adult mutants despite their hyperphagia. Moreover, age-dependent reductions in the energy cost of physical activity could contribute to the subsequent development of late-onset obesity in 5-HT(2C) receptor mutant mice.