Validation of Accelerometer Wear and Nonwear Time Classification Algorithm

BackgroundFive accelerometer-derived methods of identifying nonwear and wear time were compared with a self-report criterion in adults ≥ 56 years of age.MethodsTwo hundred participants who reported wearing an Actical™ activity monitor for four to seven consecutive days and provided complete daily log sheet data (i.e., the criterion) were included. Four variables were obtained from log sheets: 1) dates the device was worn; 2) time(s) the participant put the device on each day; 3) time(s) the participant removed the device each day; and 4) duration of self-reported nonwear each day. Estimates of wear and nonwear time using 60, 90, 120, 150 and 180 minutes of consecutive zeroes were compared to estimates derived from log sheets.ResultsCompared with the log sheet, mean daily wear time varied from -84, -43, -24, -14 and -8 min/day for the 60-min, 90-min, 120-min, 150-min and 180-min algorithms, respectively. Daily log sheets indicated 8.5 nonwear bouts per week with 120-min, 150-min and 180-min algorithms estimating 8.2-8.9 nonwear bouts per week. The 60-min and 90-min methods substantially overestimated number of nonwear bouts per week and underestimated time spent in sedentary behavior. Sensitivity (number of compliant days correctly identified as compliant) improved with increasing minutes of consecutive zero counts and stabilized at the 120-min algorithm. The proportion of wear time being sedentary and absolute and proportion of time spent in physical activity of varying intensities were nearly identical for each method.ConclusionsUtilization of at least 120 minutes of consecutive zero counts will provide dependable population-based estimates of wear and nonwear time, and time spent being sedentary and active in older adults wearing the Actical™ activity monitor.

PURPOSE: Few studies have evaluated whether associations with health risks differ between accelerometer and questionnaire measures of physical activity (PA) and sedentary behavior (SB), which was the objective of this study. METHODS: We followed 5,992 women (mean age 79 yr; 49.8% white, 33.3% black, 16.9% Hispanic) for all-cause mortality in the Objective PA and Cardiovascular Health Study. Vector magnitude counts/15 sec epoch from a hip worn ActiGraph GT3X+ triaxial accelerometer (required ≥4 of 7 days with ≥10 hr/d wear) were used to define time spent in SB (<19 counts/15 sec), light (19-518), moderate to vigorous (MVPA; ≥519), and total PA (≥19). The CHAMPS and CARDIA questionnaires were used to obtain detailed self-reports on PA and SB, respectively. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for a 30-min/day increment in PA or SB, controlling for age, race-ethnicity, education, smoking, number of comorbidities, self-rated health and SF36 physical function score (and awake wear time for accelerometer model). RESULTS: Mean time (min/d) from the accelerometer (wear time adjusted) and questionnaire were 337.9 and 600.4 for total PA, 287.3 and 337.8 for light PA, 50.7 and 222.6 for MVPA, and 555.7 and 482.7 for SB. Wear time-adjusted Spearman correlations between these measures were 0.29, 0.16, 0.34, and 0.28 for total, light, MVPA, and SB, respectively. There were 706 (11.9%) deaths documented during a mean 4.5 year follow-up. HRs (95% CIs) for accelerometer and questionnaire were 0.88 (0.87, 0.91) and 0.98 (0.97, 0.99) for total PA; 0.88 (0.85, 0.91) and 0.98 (0.97, 0.99) for light PA, 0.65 (0.59, 0.72) and 0.98 (0.97, 0.99) for MVPA, and 1.14 (1.10, 1.17) and 1.02 (1.01, 1.03) for SB. Associations did not meaningfully differ when stratified on categories of race-ethnicity (white, black, Hispanic) or age (<80 vs ≥80 year). CONCLUSIONS: Associations with all-cause mortality risk are stronger for accelerometer compared with questionnaire measures of PA and SB. The differences in strength of associations and the modest correlations between accelerometer and questionnaire measures suggest less precision with questionnaires and that accelerometer measures are capturing health-promoting aspects of movement in older women that are not captured in widely used questionnaires.

Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants. The analysis was conducted on accelerometer data from 32 healthy infants (4-25wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland-Altman plots. Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and -0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838-0.842) and specificity (0.948, 95% confidence interval, 0.944-0.948). Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than -0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.

This study examined the 1-yr test-retest reliability and criterion validity of sedentary time survey items in a subset of participants from a large, nationwide prospective cohort. Participants included 423 women and 290 men age 31 to 72 yr in the Cancer Prevention Study-3. Reliability was assessed by computing Spearman correlation coefficients between responses from prestudy and poststudy surveys. Validity was assessed by comparing survey-estimated sedentary time with a latent variable representing true sedentary time estimated from the 7-d diaries, accelerometry, and surveys through the method of triads. Sensitivity analyses were restricted to 566 participants with an average of 14+ h of diary and accelerometer data per day for 7 d per quarter. Reliability estimates for total sitting time were moderate or strong across all demographic strata (Spearman ρ ≥ 0.6), with significant differences by race (P = 0.01). Reliability estimates were strongest for the TV-related sedentary time item (Spearman ρ, 0.74; 95% confidence interval, 0.70-0.77). The overall validity coefficient (VC) for survey-assessed total sedentary time was 0.62 (95% confidence interval, 0.55-0.69), although VC varied by age group and activity level (P < 0.05). However, VC were similar across groups (P < 0.05) when restricting to highly compliant participants in a sensitivity analysis. The Cancer Prevention Study-3 sedentary behavior questionnaire has acceptable reliability and validity for ranking or categorizing participants according to sedentary time. Acceptable reliability and validity estimates persist across various demographic subgroups.

Despite the many health benefits of being physically active nearly a quarter of U.S. adults and adolescents report no participation in leisure-time physical activity. It is recommended that children and adolescents participate in physical activity for at least 60 minutes daily at moderate or vigorous intensity. In addition to potentially enhancing academic performance participation in physical activity may also influence perceptions of physical appearance and global self-esteem which tend to decrease with age in adolescent females. The purposes of this study were to: (a) examine the prevalence of overweight and obesity and the percentage of adolescent females from a rural community that meets physical activity recommendations; (b) examine the relationship of body composition physical activity and sedentary behavior on academic performance; and (c) examine the relationship between physical activity and physical self-concept among adolescent females. Thirty adolescent females (mean age = 15.6 ± 1.3 years) wore an Actigraph GT1M accelerometer for seven consecutive days set to measure in 15-second epochs. Age specific cutpoints were used to determine minutes of sedentary behavior and light moderate vigorous and moderate-to-vigorous physical activity (MVPA). Participants completed the Physical Self-Description Questionnaire (PSDQ) to assess how they perceive themselves physically. Height weight and percent body fat were measured. Grade point averages (GPA) were obtained from school records. Regression analysis was performed to predict GPA from measures of body composition sedentary behavior and MVPA. Measures of physical activity and sedentary behavior were correlated with PSDQ subscale scores with Pearson correlations. Thirty percent of participants (n = 9) were obese (BMI [greater than or equal to] 95th percentile) and another 36% (n = 11) were overweight (BMI between the 85th and 94th percentiles). None of the participants accumulated [greater than or equal to] 60 minutes of MVPA per day. Daily MVPA averaged 9.7 (± 7.1) minutes. Percent fat (r = -.51) minutes of MVPA (r = .34) and time spent in sedentary behavior (r = .32) were significantly correlated (p < .05) with GPA. Together measures of body composition physical activity and sedentary behavior explained 36% of the variance (multiple R = .60) in GPA. The standard error of estimate for predicting GPA was 0.64. Minutes per day spent in light physical activity was negatively correlated (p < .01) with the Self-Esteem (r = -.51) Body Fat (r = -.52) and Global Physical Self-Concept (r = -.48) subscales of the PSDQ. Only the Physical Activity subscale of the PSDQ was significantly correlated with MVPA (r = .36). In conclusion the current sample of adolescent females had a high prevalence of overweight and obesity and was physically inactive. Academic performance was significantly associated with measures of body composition physical activity and sedentary behavior. Time spent in light physical activity was associated with lower self-perceptions of body fat global physical self-concept and self-esteem. Measures of higher intensity physical activity were generally not associated with physical self-perception measures possibly due to the low amount of time spent in MVPA. Interventions to increase physical activity and improve body composition in adolescent girls should be considered not only for their health effects but also for their potential to impact academic performance and psychological profiles.

BackgroundPhysical activity and sedentary behaviour among children should be measured accurately in order to investigate their relationship with health. Accelerometry provides objective and accurate measurement of body movement, which can be converted to meaningful behavioural outcomes. The aim of this study was to evaluate the best evidence for the decisions on data collection and data processing with accelerometers among children resulting in a standardized protocol for use in the participating countries.Methods/DesignThis cross-sectional accelerometer study was conducted as part of the European ENERGY-project that aimed to produce an obesity prevention intervention among schoolchildren. Five countries, namely Belgium, Greece, Hungary, Switzerland and the Netherlands participated in the accelerometer study. We used three different Actigraph models-Actitrainers (triaxial), GT3Xs and GT1Ms. Children wore the device for six consecutive days including two weekend days. We selected an epoch length of 15 seconds. Accelerometers were placed at children's waist at the right side of the body in an elastic belt.In total, 1082 children participated in the study (mean age = 11.7 ± 0.75 y, 51% girls). Non-wearing time was calculated as periods of more than 20 minutes of consecutive zero counts. The minimum daily wearing time was set to 10 hours for weekdays and 8 hours for weekend days. The inclusion criterion for further analysis was having at least three valid weekdays and one valid weekend day. We selected a cut-point (count per minute (cpm)) of <100 cpm for sedentary behaviour, <3000 cpm for light, <5200 cpm for moderate, and >5200 cpm for vigorous physical activity. We also created time filters for school-time during data cleaning in order to explore school-time physical activity and sedentary behaviour patterns in particular.DiscussionThis paper describes the decisions for data collection and processing. Use of standardized protocols would ease future use of accelerometry and the comparability of results between studies.

There is little consensus on how many hours of accelerometer wear time is needed to reflect a usual day. This study identifies the bias in daily physical activity (PA) estimates caused by accelerometer wear time. 124 adults (age = 41 ± 11 years; BMI = 27 ± 7 kg·m⁻²) contributed approximately 1,200 days accelerometer wear time. Five 40 day samples were randomly selected with 10, 11, 12, 13, and 14 h·d⁻¹ of wear time. Four semisimulation data sets (10, 11, 12, 13 h·d⁻¹) were created from the reference 14 h·d⁻¹ data set to assess Absolute Percent Error (APE). Repeated-measures ANOVAs compared min·d⁻¹ between 10, 11, 12, 13 h·d⁻¹ and the reference 14 h·d⁻¹ for inactivity (<100 cts·min⁻¹), light (100-1951 cts·min⁻¹), moderate (1952-5724 cts·min⁻¹), and vigorous (≥5725 cts·min⁻¹) PA. APE ranged from 5.6%-41.6% (10 h·d⁻¹ = 28.2%-41.6%; 11 h·d⁻¹ = 20.3%-36.0%; 12 h·d⁻¹ = 13.5%-14.3%; 13 h·d⁻¹ = 5.6%-7.8%). Min·d⁻¹ differences were observed for inactivity, light, and moderate PA between 10, 11, 12, and 13 h·d⁻¹ and the reference (P < .05). This suggests a minimum accelerometer wear time of 13 h·d⁻¹ is needed to provide a valid measure of daily PA when 14 h·d⁻¹ is used as a reference.

Determining accelerometer non-wear and sedentary time in midlife and older adults

This investigation sought to determine how accelerometer wear (1)biased estimates of sedentary behavior (SB) and physical activity (PA), (2)affected misclassifications for meeting the Physical Activity Guidelines for Americans, and (3)impacted the results of regression models examining the association between moderate to vigorous physical activity (MVPA) and a clinically relevant health outcome. A total of 100 participants [age: 20.6 (7.9) y] wore an ActiGraph GT3X+ accelerometer for 15.9 (1.6) hours per day (reference dataset) on the hip. The BOD POD was used to determine body fat percentage. A data removal technique was applied to the reference dataset to create individual datasets with wear time ranging from 15 to 10 hours per day for SB and each intensity of PA. Underestimations of SB and each intensity of PA increased as accelerometer wear time decreased by up to 167.2minutes per day. These underestimations resulted in Physical Activity Guidelines for Americans misclassification rates of up to 42.9%. The regression models for the association between MVPA and body fat percentage demonstrated changes in the estimates for each wear-time adherence level when compared to the model using the reference MVPA data. Increasing accelerometer wear improves daily estimates of SB and PA, thereby also improving the precision of statistical inferences that are made from accelerometer data.

BackgroundMore and more researchers have started to analyse device-measured physical activity data using compositional data analysis (CoDA), which has led to that the effect of relative time in different behaviours can be explored. However, there are challenges related to the interpretation of the results based on CoDA. This is partly related to that CoDA provides estimates based on the relative time that is difficult to interpret relative to the 2020 guidelines of physical activity and sedentary behaviour. Since many data cohorts do not have data on sleep, the proportion of time in physical activity may vary depending on accelerometer wear time. Therefore, there is a need to explore cut-points for relative time to distinguish between individuals that do and do not reach 150–300 min of moderate-to-vigorous intensity physical activity (MVPA) per week. The aim was to establish a ratio of MVPA to awaken time that corresponds to meeting the 2020 guidelines of physical activity and sedentary behaviour in adults.MethodTo estimate the cut-off points of relative time in MVPA, the publicly available data from NHANES 2003–2004 was used and cut-off points were explored in different subsets of the total population. Values for sensitivity, specificity and cut-off values were explored; i) in total sample, ii) by tertiles of wear time, iii) in individuals with ± 5 min from 150 to 300 min of MVPA, iiii) in individuals with ± 5 min from 150 to 300 min of MVPA in the middle tertile of wear time.ResultsOverall, the analyses show high values for sensitivity (88–100%) and specificity (66–99%) for different cut-off values associated with 150–300 min of MVPA. Spending 2.4–4.7% of the time awake in MVPA was found to correspond to the 2020 guidelines of physical activity and sedentary behaviour.ConclusionBased on publicly available data from NHANES 2003–2004, spending 2.4–4.7% of time awake in MVPA corresponds to meeting the 2020 guidelines of physical activity and sedentary behaviour.

BackgroundAccelerometers are widely used to measure sedentary time and daily physical activity (PA). However, data collection and processing criteria, such as non-wear time rules might affect the assessment of total PA and sedentary time and the associations with health variables. The study aimed to investigate whether the choice of different non-wear time definitions would affect the outcomes of PA levels in youth.MethodsSeventy-seven healthy youngsters (44 boys), aged 10–17 years, wore an accelerometer and kept a non-wear log diary during 4 consecutives days. We compared 7 published algorithms (10, 15, 20, 30, 60 min of continuous zeros, Choi, and Troiano algorithms). Agreements of each algorithm with the log diary method were assessed using Bland-Altmans plots and by calculating the concordance correlation coefficient for repeated measures.ResultsVariations in time spent in sedentary and moderate to vigorous PA (MVPA) were 30 and 3.7%. Compared with the log diary method, greater discrepancies were found for the algorithm 10 min (p < 0.001). For the time assessed in sedentary, the agreement with diary was excellent for the 4 algorithms (Choi, r = 0.79; Troiano, r = 0.81; 30 min, r = 0.79; 60 min, r = 0.81). Concordance for each method was excellent for the assessment of time spent in MVPA (> 0.86). The agreement for the wear time assessment was excellent for 5 algorithms (Choi r = 0.79; Troiano r = 0.79; 20 min r = 0.77; 30 min r = 0.80; 60 min r = 0.80).ConclusionsThe choice of non-wear time rules may considerably affect the sedentary time assessment in youth. Using of appropriate data reduction decision in youth is needed to limit differences in associations between health outcomes and sedentary behaviors and may improve comparability for future studies. Based on our results, we recommend the use of the algorithm of 30 min of continuous zeros for defining non-wear time to improve the accuracy in assessing PA levels in youth.Trial registrationNCT02844101 (retrospectively registered at July 13th 2016).

Association between urinary incontinence and device-measured physical activity: a cross-sectional study.

BackgroundPhysical activity levels in childhood have decreased, making the promotion of children’s physical activity an important issue. The present study examined gender and grade differences in objectively measured sedentary behavior, physical activity, and physical activity guideline attainment among Japanese children and adolescents.MethodsIn total, 329 boys and 362 girls age 3–15 years completed the survey. School grade, gender, height, and weight were collected by questionnaires and physical activity objectively measured using an accelerometer (Lifecorder Suzuken Co.). Physical activity level (in MET) was classified as sedentary (<1.5), light (≥1.5 to <3), moderate (≥3 to <6), or vigorous (≥6). Continuous zero accelerometer counts for ≥20 min were censored and a valid accelerometry study required at least 3 days (2 weekdays and 1 weekend day) with > 600 min/day total wear time. Two-way analysis of covariance and logistic regression analyses, adjusted for weight status and accelerometer wear time, were used to examine gender and grade differences in physical activity variables and the likelihood of physical activity guideline attainment by gender and grade level.ResultsParticipants were sedentary 441.4 (SD, 140.1) min/day or 53.7 % of the average daily accelerometer wear time of 811.2 (118.7) min, engaged in light physical activity 307.1 (70.0) min or 38.4 % of wear time, moderate physical activity 34.6 (14.8) min (4.3 %), vigorous physical activity 28.3 (19.1) min (3.6 %), and took 12462.6 (4452.5) steps/day. Boys were more physically active and took more steps/day than girls. Students in higher grades were less active than those in lower grades. Boys were significantly more likely to meet physical activity guidelines than girls (OR: 2.07, 95 % CI: 1.45–2.96). Preschoolers (6.66, 4.01–11.06), lower-grade elementary school students (17.11, 8.80–33.27), and higher-grade elementary school students (7.49, 4.71–11.92) were more likely to meet guidelines than junior high school students.ConclusionsBoys and lower-grade students engaged in more physical activity and were more likely to attain guidelines than girls and higher-grade students. These findings highlight the need for effective and sustainable strategies to promote physical activity in Japanese school children.

BackgroundThe effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users’ subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior.ObjectiveThis study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins).MethodsFour databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non–digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association.ResultsOverall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis).ConclusionsThe findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak.

The life expectancy of adults with mental illness is significantly less than that of the general population. This is largely due to poor physical health. Physical activity is consistently recommended for the prevention and management of non-communicable diseases and also has mental health benefits. The aim of this thesis was to understand and promote physical activity in adults with mental illness, to improve physical health. Study One was a cross-sectional study of inpatients in a private hospital. It assessed the (i) feasibility of self-report and objective measurement of physical activity and sedentary behaviour, (ii) levels of physical activity and sedentary behaviour, and (iii) physical activity attitudes and preferences for contexts and sources of support. 101 participants completed questionnaires on physical activity and sitting time, activity preferences and attitudes, psychological distress and sociodemographic and health variables. 38 also wore an accelerometer for 7 consecutive days. Feasibility of measurement was assessed in terms of participant engagement; self-reported ease/difficulty; extreme self-report data values; and adherence to accelerometer wear time criteria. Findings demonstrated that inpatient adults with mental illness can engage with both questionnaire and accelerometry measurement, that it was more feasible but less acceptable to wear an accelerometer than to complete questionnaires, and that this was not influenced by level of psychological distress. Questionnaire data were used to determine time spent in (i) walking and moderate- and vigorous-intensity activity (MVPA), and (ii) domain specific sitting time. Accelerometry was used to determine mean daily time spent in MVPA and sedentary behaviour. Bivariate associations between self-reported MVPA, sedentary behaviour and explanatory variables of gender, age, education, body mass and distress were analysed using regression analyses. Self-report data indicated a median of 32 minutes/day in MVPA and a median of 761 minutes/day in sedentary behaviour. Accelerometry data indicated an average of 37 minutes/day in MVPA and 664 minutes/day in sedentary behaviour. Analyses indicated no significant associations between explanatory variables and MVPA or sedentary behaviour. Questionnaire data were used to determine (i) physical activity interest; (ii) reasons to do activity; (iii) general knowledge regarding activity benefits; (iv) preferences for activity type, context and sources of support; and (v) activity barriers. More than three quarters (77%) of participants expressed high interest to do physical activity while in hospital, with the most common reasons being to maintain physical health and improve emotional wellbeing (≥95%). More than 90% of participants agreed physical activity was beneficial for managing psychological wellbeing, heart disease, stress, diabetes and quality of life; but fewer than half agreed that activity had benefits for serious mental illness. Participants preferred walking; activity that can be done alone, at a fixed time and with a set routine and format; and a personal trainer, physiotherapist or an exercise physiologist to recommend, design or lead physical activity programs. Major barriers were fatigue and lack of motivation. There were no significant preference differences by level of psychological distress. Study Two was a nurse-led, two stage single group intervention trial that evaluated the effectiveness of a behavioural counselling program on improving metabolic health indicators, physical activity levels and psychosocial wellbeing of outpatient adults with mental illness. Participants received counselling every three weeks during stage one (19 weeks) and every six weeks during stage two (additional 12 weeks), and attended progress review sessions with a medical practitioner every six weeks. Assessment included self-report questionnaires of physical activity, sedentary behaviour and psychosocial wellbeing; objective measurement of physical activity and sedentary behaviour; blood pressure and anthropometric measurement. Of the 21 participants who consented, 16 completed stage one and 10 completed stage two of the intervention. During stage one, there were statistically significant improvements in waist circumference (-2.7cm, 95%CI -5.15, -0.22, p<0.035), and psychological quality of life (9.14, 95%CI 0.10, 18.18, p=0.048). During stage two, there were statistically significant reductions in waist circumference (-7.1cm, 95%CI 1.17, 12.93, p=0.024) and weight (-5.51kg, 95%CI 1.07, 9.95, p=0.033). Conclusions: Inpatient adults with mental illness are interested in activity programs and can achieve good levels of physical activity. Inpatients can engage with activity questionnaires and monitors, but may be reluctant to wear activity monitors and find sedentary behaviour questionnaires difficult. It is recommended that inpatient activity programs highlight the benefits for serious mental illness, focus on walking, be led by staff with exercise expertise, and include strategies to allow for fatigue and support motivation. There is a need for inpatient interventions to reduce sedentary behaviour. Physical activity counselling may be an effective strategy for improving the physical health of adults with mental illness, and can reduce waist circumference and weight, and improve quality of life. It is recommended that behavioural counselling programs involve face-to-face sessions at a frequency of least every three weeks, be sustained over time, and have an ‘open door’ policy to allow for attendance interruptions that may be caused by deteriorations in mental or physical health.