Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.

BACKGROUND: Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts. METHODS: We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL. RESULTS: In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes). CONCLUSIONS: STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

Associations of Wearable Activity Tracker Use With Physical Activity and Health Outcomes in Patients With Cancer: Findings from a Population-Based Survey Study.

BACKGROUND: Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown. OBJECTIVE: This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer. METHODS: Data from 957 cancer survivors from the Health Information National Trends Survey-Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights. RESULTS: Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression. CONCLUSIONS: This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion.

Digital Health Engagement in the US Population: Insights From the 2018 Health Information National Trends Survey.

Objectives. To examine prevalence and predictors of digital health engagement among the US population. Methods. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698-3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Results. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one's health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09-4.21; P value range < .001-.03). Conclusions. Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention.

Feasibility of FitSurvivor: A technology-enhanced group-based fitness intervention for adolescent and young adult survivors of childhood cancer.

BACKGROUND: This study evaluated the feasibility of a technology-enhanced group-based fitness intervention for adolescent and young adult (AYA) survivors of childhood cancer. PROCEDURE: AYA survivors ages 13-25 years were randomized to the intervention (eight in-person group sessions with mobile app and FitBit followed by 4 weeks of app and FitBit only) or waitlist control. Assessments were at 0, 2, 3, 6, and 9 months. Feasibility was evaluated by enrollment, retention, attendance, app engagement, and satisfaction. Secondary outcomes included physical activity, muscular strength/endurance, cardiorespiratory fitness, health-related quality of life, and fatigue. RESULTS: A total of 354 survivors were mailed participation letters; 68 (19%) were screened, of which 56 were eligible and 49 enrolled (88% of those screened eligible, 14% of total potentially eligible). Forty-nine survivors (Mage  = 18.5 years, 49% female) completed baseline assessments and were randomized (25 intervention, 24 waitlist). Thirty-seven (76%) completed the postintervention assessment and 32 (65%) completed the final assessment. On average, participants attended 5.7 of eight sessions (range 1-8). Overall intervention satisfaction was high (M = 4.3, SD = 0.58 on 1-5 scale). Satisfaction with the companion app was moderately high (M = 3.4, SD = 0.97). The intervention group demonstrated significantly greater improvement in lower body muscle strength compared to the waitlist postintervention, and small but not statistically significant changes in other secondary measures. CONCLUSIONS: A group-based intervention with a mobile app and fitness tracker was acceptable but has limited reach due to geographical barriers and competing demands experienced by AYA survivors.

Patient-generated health data collection using a wearable activity tracker in cancer patients-a feasibility study.

PURPOSE: Incorporation of patient-generated health data (PGHD) into clinical research requires an investigation of the validity of outcomes and feasibility of implementation. This single-arm pilot trial investigated the feasibility of using a commercially available activity tracking wearable device in cancer patients to assess adherence to the device and real-time PGHD collection in a clinical research setting. METHODS: From July to November 2017, enrolled adult patients were asked to wear a wristband-style device. Brief Fatigue Inventory (BFI) and MD Anderson Symptom Inventory (MDASI) were assessed at baseline and on day 29. Furthermore, 29-day Pittsburgh Sleep Quality Index, global impression of the devices, and NCI CTCAE v4 were evaluated. RESULTS: Of 30 patients (mean age, 58.6 years; male, 21 [70%]), 15 (50%) and 11 (36.7%) had gastrointestinal and lung cancer, respectively, and 27 (90%, 95% CI: 0.74-0.98) were well adhered (> 70%) to the device for 28 days. The mean adherence was 84.9% (range: 41.7-95.2%). More frequent PGHD synchronization tended to show better device adherence, with moderate correlation (r = 0.62, 95% CI: 0.33-0.80, p < 000.1). CONCLUSIONS: The feasibility of using a wearable activity tracker was confirmed in cancer patients receiving chemotherapy for a month. For future implementation in clinical trials, there is a need for further comprehensive assessment of the validity and reliability of wearable activity trackers. TRIAL REGISTRATION: This trial was registered at the University Hospital Medical Information Network Clinical Trials Registry as UMIN: UMIN000027575.

Using the Technology Acceptance Model to Explore Adolescents' Perspectives on Combining Technologies for Physical Activity Promotion Within an Intervention: Usability Study.

BACKGROUND: Wearable activity trackers and social media have been identified as having the potential to increase physical activity among adolescents, yet little is known about the perceived ease of use and perceived usefulness of the technology by adolescents. OBJECTIVE: The aim of this study was to use the technology acceptance model to explore adolescents' acceptance of wearable activity trackers used in combination with social media within a physical activity intervention. METHODS: The Raising Awareness of Physical Activity study was a 12-week physical activity intervention that combined a wearable activity tracker (Fitbit Flex) with supporting digital materials that were delivered using social media (Facebook). A total of 124 adolescents aged 13 to 14 years randomized to the intervention group (9 schools) participated in focus groups immediately post intervention. Focus groups explored adolescents' perspectives of the intervention and were analyzed using pen profiles using a coding framework based on the technology acceptance model. RESULTS: Adolescents reported that Fitbit Flex was useful as it motivated them to be active and provided feedback about their physical activity levels. However, adolescents typically reported that Fitbit Flex required effort to use, which negatively impacted on their perceived ease of use. Similarly, Facebook was considered to be a useful platform for delivering intervention content. However, adolescents generally noted preferences for using alternative social media websites, which may have impacted on negative perceptions concerning Facebook's ease of use. Perceptions of technological risks included damage to or loss of the device, integrity of data, and challenges with both Fitbit and Facebook being compatible with daily life. CONCLUSIONS: Wearable activity trackers and social media have the potential to impact adolescents' physical activity levels. The findings from this study suggest that although the adolescents recognized the potential usefulness of the wearable activity trackers and the social media platform, the effort required to use these technologies, as well as the issues concerning risks and compatibility, may have influenced overall engagement and technology acceptance. As wearable activity trackers and social media platforms can change rapidly, future research is needed to examine the factors that may influence the acceptance of specific forms of technology by using the technology acceptance model. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12616000899448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations.

Accelerometer cut points are an important consideration for distinguishing the intensity of activity into categories such as moderate and vigorous. It is well-established in the literature that these cut points depend on a variety of factors, including age group, device, and wear location. The Actigraph GT9X is a newer model accelerometer that is used for physical activity research, but existing cut points for this device are limited since it is a newer device. Furthermore, there is not existing data on cut points for the GT9X at the ankle or foot locations, which offers some potential benefit for activities that do not involve arm and/or core motion. A total of N = 44 adults completed a four-stage treadmill protocol while wearing Actigraph GT9X sensors at four different locations: foot, ankle, wrist, and hip. Metabolic Equivalent of Task (MET) levels assessed by indirect calorimetry along with Receiver Operating Characteristic (ROC) curves were used to establish cut points for moderate and vigorous intensity for each wear location of the GT9X. Area under the ROC curves indicated high discrimination accuracy for each case.

Validity of Garmin Vívofit 1 and Garmin Vívofit 3 for School-Based Physical Activity Monitoring.

PURPOSE: Activity trackers are useful tools for physical activity promotion in adolescents, but robust validity evaluations have not been done under free-living conditions. This study evaluated the validity of the Garmin Vívofit 1 (G1) and Garmin Vívofit 3 (G3) in different settings and contexts. METHODS: The participants (girls: 52%, age: 15.9 [1.9] y) wore the G1 and G3 on their nondominant wrist and the Yamax pedometer on their right hip for a period of 1 week. Validity was examined in 4 discrete segments (before school, in school, after school, and whole day). The criterion method was the Yamax pedometer. RESULTS: Both the G1 and G3 could be considered equivalent to the Yamax pedometer regarding the before school, in school, and whole day segments. The G1 showed wider limits of agreement than G3. CONCLUSIONS: The G1 and G3 trackers exhibited acceptable validity for 3 of the 4 segments (before school, in school, and whole day measurements). The results were less accurate during the after-school segment. The evidence that the validity of the monitors varied depending on the setting and context is an important consideration for research on adolescent activity patterns.

Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies.

BACKGROUND: Wearable physical activity (PA) trackers are becoming increasingly popular for intervention and assessment in health promotion research and practice. The purpose of this article is to present lessons learned from four studies that used commercial PA tracking devices for PA intervention or assessment, present issues encountered with their use, and provide guidelines for determining which tools to use. METHOD: Four case studies are presented that used PA tracking devices (iBitz, Zamzee, FitBit Flex and Zip, Omron Digital Pedometer, Sensewear Armband, and MisFit Flash) in the field-two used the tools for intervention and two used the tools as assessment methods. RESULTS: The four studies presented had varying levels of success with using PA devices and experienced several issues that impacted their studies, such as companies that went out of business, missing data, and lost devices. Percentage ranges for devices that were lost were 0% to 29% and was 0% to 87% for those devices that malfunctioned or lost data. CONCLUSIONS: There is a need for low-cost, easy-to-use, accurate PA tracking devices to use as both intervention and assessment tools in health promotion research related to PA.

Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 activity patterns. The second part models the proportions when they are in interval (0,1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this 3-part model, the regression structures are specified as smooth curves measured at various time points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the 3 longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

Accelerometer-Derived Activity Phenotypes in Young Adults: a Latent Class Analysis.

PURPOSE: To identify "activity phenotypes" from accelerometer-derived activity characteristics among young adults. METHODS: Participants were young adults (n = 628, mean age, 22.1, SD 0.6) in the Raine Study in Western Australia. Sex-specific latent class analyses identified sub-groups using eight indicators derived from 7-day hip-worn Actigraph GT3X+ accelerometers: daily steps, total daily moderate-to-vigorous physical activity (MVPA), MVPA variation, MVPA intensity, MVPA bout duration, sedentary-to-light ratio, sedentary-to-light ratio variation, and sedentary bout duration. RESULTS: Five activity phenotypes were identified for women (n = 324) and men (n = 304). Activity phenotype 1 for both women (35%) and men (30%) represented average activity characteristics. Phenotype 2 for women (17%) and men (16%) was characterized by below average total activity and MVPA (10.6 and 16.7 min of MVPA/day, women and men respectively). Phenotype 3 for women (15%) and men (23%) was characterized by below average total physical activity, average MVPA (32.6 and 36.5 min/day), high sedentary-light ratio and long sedentary bouts. Phenotype 4 differed between women (29%) and men (18%) but both had low sedentary-to-light ratios and shorter sedentary bouts. Finally, phenotype 5 in both women (4%) and men (12%) was characterized by extreme MVPA metrics (81.3 and 96.1 min/day). CONCLUSIONS: Five activity phenotypes were identified for each gender in this population of young adults which can help design targeted interventions to enhance or modulate activity phenotypes.

Diffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey.

BACKGROUND: With the ever-increasing availability of mobile apps, consumer wearables, and smart medical devices, more and more individuals are self-tracking and managing their personal health data. OBJECTIVE: The aim of this study was to investigate the diffusion of the digital self-tracking movement in Canada. It provides a comprehensive, yet detailed account of this phenomenon. It examines the profile of digital self-trackers, traditional self-trackers, and nontrackers, further investigating the primary motivations for self-tracking and reasons for nontracking; barriers to adoption of connected care technologies; users' appreciation of their self-tracking devices, including what they perceive to be the main benefits; factors that influence people's intention to continue using connected care technologies in the future; and the reasons for usage discontinuance. METHODS: We conducted an online survey with a sample of 4109 Canadian adults, one of the largest ever. To ensure a representative sample, quota method was used (gender, age), following stratification by region. The maximum margin of error is estimated at 1.6%, 19 times out of 20. RESULTS: Our findings reveal that 66.20% (2720/4109) of our respondents regularly self-track one or more aspects of their health. About one in 4 respondents (1014/4109, 24.68%) currently owns a wearable or smart medical device, and 57.20% (580/1014) use their devices on a regular basis for self-tracking purposes. Digital self-trackers are typically young or mature adults, healthy, employed, university educated, with an annual family income of over $80,000 CAD. The most popular reported device is the fitness tracker or smartwatch that can capture a range of parameters. Currently, mobile apps and digital self-tracking devices are mainly used to monitor physical activity (856/1669, 51.13%), nutrition (545/1669, 32.65%), sleep patterns (482/1669, 28.88%) and, to a much lesser extent, cardiovascular and pulmonary biomarkers (215/1669, 12.88%), medication intake (126/1669, 7.55%), and glucose level (79/1669, 4.73%). Most users of connected care technologies (481/580, 83.0%) are highly satisfied and 88.2% (511/580) intend to continue using their apps and devices in the future. A majority said smart digital devices have allowed them to maintain or improve their health condition (398/580, 68.5%) and to be better informed about their health in general (387/580, 66.6%). About 33.80% of our sample (1389/4109) is composed of people who do not monitor their health or well-being on a regular basis. CONCLUSIONS: Our study shows an opportunity to advance the health of Canadians through connected care technologies. Our findings can be used to set baseline information for future research on the rise of digital health self-tracking and its impacts. Although the use of mobile apps, consumer wearables, and smart medical devices could potentially benefit the growing population of patients with chronic conditions, the question remains as to whether it will diffuse broadly beyond early adopters and across cost inequities.

A qualitative evaluation of breast cancer survivors' acceptance of and preferences for consumer wearable technology activity trackers.

BACKGROUND: Physical inactivity and sedentary behaviour are common amongst breast cancer survivors. These behaviours are associated with an increased risk of comorbidities such as heart disease, diabetes and other cancers. Commercially available, wearable activity trackers (WATs) have potential utility as behavioural interventions to increase physical activity and reduce sedentary behaviour within this population. PURPOSE: The purpose of the study is to explore the acceptability and usability of consumer WAT amongst postmenopausal breast cancer survivors. METHODS: Fourteen participants tested two to three randomly assigned trackers from six available models (Fitbit One, Jawbone Up 24, Garmin Vivofit 2, Garmin Vivosmart, Garmin Vivoactive and Polar A300). Participants wore each device for 2 weeks, followed by a 1-week washout period before wearing the next device. Four focus groups employing a semi-structured interview guide explored user perceptions and experiences. We used a thematic analysis approach to analyse focus group transcripts. RESULTS: Five themes emerged from our data: (1) trackers' increased self-awareness and motivation, (2) breast cancer survivors' confidence and comfort with wearable technology, (3) preferred and disliked features of WAT, (4) concerns related to the disease and (5) peer support and doctor monitoring were possible strategies for WAT application. CONCLUSIONS: WATs are perceived as useful and acceptable interventions by postmenopausal breast cancer survivors. Effective WAT interventions may benefit from taking advantage of the simple features of the trackers paired with other behavioural change techniques, such as specialist counselling, doctor monitoring and peer support, along with simple manual instructions.

Users' experiences of wearable activity trackers: a cross-sectional study.

BACKGROUND: Wearable activity trackers offer considerable promise for helping users to adopt healthier lifestyles. This study aimed to explore users' experience of activity trackers, including usage patterns, sharing of data to social media, perceived behaviour change (physical activity, diet and sleep), and technical issues/barriers to use. METHODS: A cross-sectional online survey was developed and administered to Australian adults who were current or former activity tracker users. Results were analysed descriptively, with differences between current and former users and wearable brands explored using independent samples t-tests, Mann-Whitney, and chi square tests. RESULTS: Participants included 200 current and 37 former activity tracker users (total N = 237) with a mean age of 33.1 years (SD 12.4, range 18-74 years). Fitbit (67.5%) and Garmin devices (16.5%) were most commonly reported. Participants typically used their trackers for sustained periods (5-7 months) and most intended to continue usage. Participants reported they had improved their physical activity (51-81%) more commonly than they had their diet (14-40%) or sleep (11-24%), and slightly more participants reported to value the real time feedback (89%) compared to the long-term monitoring (78%). Most users (70%) reported they had experienced functionality issues with their devices, most commonly related to battery life and technical difficulties. CONCLUSIONS: Results suggest users find activity trackers appealing and useful tools for increasing perceived physical activity levels over a sustained period.

A retrospective analysis of real-world use of the eaTracker® My Goals website by adults from Ontario and Alberta, Canada.

BACKGROUND: Little is known about use of goal setting and tracking tools within online programs to support nutrition and physical activity behaviour change. In 2011, Dietitians of Canada added "My Goals," a nutrition and physical activity behaviour goal setting and tracking tool to their free publicly available self-monitoring website (eaTracker® ( http://www.eaTracker.ca/ )). My Goals allows users to: a) set "ready-made" SMART (Specific, Measurable, Attainable, Realistic, Time-related) goals (choice of n = 87 goals from n = 13 categories) or "write your own" goals, and b) track progress using the "My Goals Tracker." The purpose of this study was to characterize: a) My Goals user demographics, b) types of goals set, and c) My Goals Tracker use. METHODS: Anonymous data on all goals set using the My Goals feature from December 6/2012-April 28/2014 by users ≥19y from Ontario and Alberta, Canada were obtained. This dataset contained: anonymous self-reported user demographic data, user set goals, and My Goals Tracker use data. Write your own goals were categorized by topic and specificity. Data were summarized using descriptive statistics. Multivariate binary logistic regression was used to determine associations between user demographics and a) goal topic areas and b) My Goals Tracker use. RESULTS: Overall, n = 16,511 goal statements (75.4 % ready-made; 24.6 % write your own) set by n = 8,067 adult users 19-85y (83.3 % female; mean age 41.1 ± 15.0y, mean BMI 28.8 ± 7.6kg/m(2)) were included for analysis. Overall, 33.1 % of ready-made goals were from the "Managing your Weight" category. Of write your own goal entries, 42.3 % were solely distal goals (most related to weight management); 38.6 % addressed nutrition behaviour change (16.6 % had unspecific general eating goals); 18.1 % addressed physical activity behaviour change (47.3 % had goals without information on exercise amount and type). Many write your own goals were poor quality (e.g., non-specific (e.g., missing amounts)), and possibly unrealistic (e.g., no sugar). Few goals were tracked (<10 %). Demographic variables had statistically significant relations with goal topic areas and My Goals Tracker use. CONCLUSIONS: eaTracker® users had high interest in goal setting and the My Goals feature, however, self-written goals were often poor quality and goal tracking was rare. Further research is needed to better support users.

The Emergence of Personalized Health Technology.

Personalized health technology is a noisy new entrant to the health space, yet to make a significant impact on population health but seemingly teeming with potential. Devices including wearable fitness trackers and healthy-living apps are designed to help users quantify and improve their health behaviors. Although the ethical issues surrounding data privacy have received much attention, little is being said about the impact on socioeconomic health inequalities. Populations who stand to benefit the most from these technologies are unable to afford, access, or use them. This paper outlines the negative impact that these technologies will have on inequalities unless their user base can be radically extended to include vulnerable populations. Frugal innovation and public-private partnership are discussed as the major means for reaching this end.

Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial.

BACKGROUND: Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA. OBJECTIVE: We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA. METHODS: This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants' own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed. RESULTS: Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04). CONCLUSIONS: Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA. TRIAL REGISTRATION: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.

Establishing a Consensus-Based Framework for the Use of Wearable Activity Trackers in Health Care: Delphi Study.

BACKGROUND: Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings. OBJECTIVE: This study aimed to develop a consensus-based framework for the optimal use of WATs in health care. METHODS: A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants' opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist's clarity, utility, and appropriateness. RESULTS: Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients' data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4. CONCLUSIONS: This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

Sleep During the COVID-19 Pandemic: Longitudinal Observational Study Combining Multisensor Data With Questionnaires.

BACKGROUND: The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people's sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep. OBJECTIVE: This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period. METHODS: We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables. RESULTS: The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (ß=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (ß=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (ß=.15, 95% CI 0.05-0.27; P=.006) and MS (ß=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (ß=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (ß=-.15, 95% CI -0.27 to -0.05; P<.001). Engaging in PA later in the day was associated with longer TST (ß=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (ß=-.01, 95% CI -0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (ß=-.26, 95% CI -0.29 to -0.23; P<.001), earlier MS (ß=-.29, 95% CI -0.33 to -0.26; P<.001), and reduced variability in TST (ß=-.16, 95% CI -0.23 to -0.09; P<.001). CONCLUSIONS: Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce.