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FITT

FITT (Frequency, Intensity, Time, and Type) is a widely accepted framework for designing and evaluating exercise programs.
It provides a structured approach to optimizing physical activity regimens based on four key components: how often (Frequency), how hard (Intensity), how long (Time), and what type of activity (Type).
This comprehensive protocol helps ensure exercise interventions are tailored to individual needs and desired outcomes, such as improved cardiovasular health, muscule strength, or weight management.
Leveraging the FITT framework can enhance the effectiveness and consistency of exercise programs, leading to better researchh outcomes and improved patient/participant results.

Most cited protocols related to «FITT»

1
Observation-Based Neural Prosthetic Control
Since paralyzed users of neural prostheses cannot generate overt arm movements an observation based algorithm training methodology can be used, as in previous animal studies8 (link) and clinical trials10 (link). We tested the ReFIT-KF algorithm with observation based training, replacing the native arm movement stage of algorithm training with an observation stage (Fig. 5b).
Observation-based decode models were built with both of the monkey’s arms comfortably restrained along his side. A previously recorded arm-controlled experimental block of 500 center-out and back trials was shown to the monkey while in this posture. The kinematics of this recording were derived from a arm-controlled session from Monkey L. To help keep the monkey engaged in the task, he was rewarded when the computer-controlled cursor acquired and held the target for 500 ms.
Under this experimental context, the neural data recorded during these observation sessions and the previously recorded cursor kinematics served as the training data to build the initial decode model. This resulting model was then run online and used as training data to build the ReFIT-KF decoder. Little to no arm movement was visually noted during both observational blocks and decoding blocks.
Performance of ReFIT-KF based control during these sessions, as measured by the Fitts’ law metric, was roughly equivalent to performance on sessions that initially trained from arm movement data.
Gilja V., Nuyujukian P., Chestek C.A., Cunningham J.P., Yu B.M., Fan J.M., Churchland M.M., Kaufman M.T., Kao J.C., Ryu S.I, & Shenoy K.V. (2012). A High-Performance Neural Prosthesis Enabled by Control Algorithm Design. Nature neuroscience, 15(12), 1752-1757.
Publication 2012
Animals ARID1A protein, human FITT Monkeys Movement Nervousness Neural Prostheses
2
Observation-Based Neural Prosthetic Control
Since paralyzed users of neural prostheses cannot generate overt arm movements an observation based algorithm training methodology can be used, as in previous animal studies8 (link) and clinical trials10 (link). We tested the ReFIT-KF algorithm with observation based training, replacing the native arm movement stage of algorithm training with an observation stage (Fig. 5b).
Observation-based decode models were built with both of the monkey’s arms comfortably restrained along his side. A previously recorded arm-controlled experimental block of 500 center-out and back trials was shown to the monkey while in this posture. The kinematics of this recording were derived from a arm-controlled session from Monkey L. To help keep the monkey engaged in the task, he was rewarded when the computer-controlled cursor acquired and held the target for 500 ms.
Under this experimental context, the neural data recorded during these observation sessions and the previously recorded cursor kinematics served as the training data to build the initial decode model. This resulting model was then run online and used as training data to build the ReFIT-KF decoder. Little to no arm movement was visually noted during both observational blocks and decoding blocks.
Performance of ReFIT-KF based control during these sessions, as measured by the Fitts’ law metric, was roughly equivalent to performance on sessions that initially trained from arm movement data.
Gilja V., Nuyujukian P., Chestek C.A., Cunningham J.P., Yu B.M., Fan J.M., Churchland M.M., Kaufman M.T., Kao J.C., Ryu S.I, & Shenoy K.V. (2012). A High-Performance Neural Prosthesis Enabled by Control Algorithm Design. Nature neuroscience, 15(12), 1752-1757.
Publication 2012
Animals ARID1A protein, human FITT Monkeys Movement Nervousness Neural Prostheses
3
Center-Out Task for Monkey Behavior
The same center-out-and-back task was run on 280 sessions across monkeys L and J, spanning at least 16 months for each monkey. Although additional experiments (using different control algorithms and behavioral tasks) may have been run on these experimental days, at least 200 trials of center-out-and-back with the ReFIT-KF control algorithm were tested. On most experimental days, the task difficulty was greater than that shown in Figure 1 and Supplementary Table 1. For the experiments documented in Figure 1, the task difficulty was selected so that the monkey could successfully complete the task with the lower quality of control afforded by the Velocity-KF algorithm.
The Fitts’ law calculation is used to provide a metric that normalizes across task difficulty. For reference, monkey L was implanted on 01/22/2008 and monkey J was implanted on 08/24/2009. Data for monkey L were collected on 182 sessions over 29 months (from 24 to 53 months post-implantation). Data for monkey J were collected on 98 sessions over 16 months (from 5 to 21 months post-implantation). Each open square and circle in Figure 2 corresponds to a single experimental day on which the index of difficulty was 1.32 (4 cm targets at 8 cm from center) and throughput was calculated from at least 40 trials of center-out to either a vertical or horizontal target. All experiments from the timespans indicated that match these criteria were included, except for days on which other experiments may have impacted animal behavior. Regression lines were fit for data from each monkey using least square regression and p-values were calculated using an ANOVA for linear regression models.
Gilja V., Nuyujukian P., Chestek C.A., Cunningham J.P., Yu B.M., Fan J.M., Churchland M.M., Kaufman M.T., Kao J.C., Ryu S.I, & Shenoy K.V. (2012). A High-Performance Neural Prosthesis Enabled by Control Algorithm Design. Nature neuroscience, 15(12), 1752-1757.
Publication 2012
FITT Monkeys neuro-oncological ventral antigen 2, human Ovum Implantation
4
Chronic Pain Severity Assessment Protocol
Chronic pain severity was measured in the group of individuals who endorsed having chronic pain and completed a detailed pain questionnaire, as part of the Q2 which included the following questions; the response categories are listed in Table 2. The four domains were selected based on their relevance to chronic pain and their role in contributing to physiological change and biological system dysregulation [2 (link), 5 (link), 24 (link)–29 (link)]:

Frequency: How often do you have this pain? (every day, ≥once a week, and ≥once a month).

Intensity: How strong would you say that the pain usually is? 10-item numeric rating scale:

0 = no pain and 10 = worst imaginable pain.

Time (duration): How long have you had this pain? (years or months).

Total pain sites: Where does it hurt? (head/face, jaw/temporomandibular joint, neck, back, shoulder, arm/elbow, hand, hip, thigh/knee/leg, ankle/foot, chest/breast, stomach, genitalia/reproductive organs, skin, or other locations).

A 50% frequency split was performed for each dimension, dichotomizing the variable to a 0 or 1 score. Chronic pain severity, based on the four frequently captured pain dimensions, frequency, intensity, time (duration), and total number of pain sites (FITT), ranged from 1 to 5 designated by a cumulative score resulting from a combined total of the dichotomized values from the four pain dimensions [0 dimensions = a value of 1; one dimension = 2; two dimensions = 3; three dimensions = 4; and four dimensions = 5]. Pain dimensions are presented in Table 2.
Sibille K.T., Steingrímsdóttir Ó.A., Fillingim R.B., Stubhaug A., Schirmer H., Chen H., McEwen B.S, & Nielsen C.S. (2016). Investigating the Burden of Chronic Pain: An Inflammatory and Metabolic Composite. Pain Research & Management, 2016, 7657329.
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Publication 2016
Ankle BAD protein, human Biopharmaceuticals Chest Chronic Pain Elbow Face FITT Foot Genitalia Head Knee Neck physiology Severity, Pain Shoulder Skin Stomach Temporomandibular Joint Thigh
5
In Vivo Brain Metabolite Quantification
The 1H-MRS data was processed offline using in-house software. The data was voxel-shifted to align the NAA grid with the VOI, then Fourier transformed in the time, AP and LR direction and Hadamard-transformed along the IS dimension. Each spectrum was frequency-aligned and zero-order phased in reference to the NAA peak. Relative levels of the i-th (i=NAA, Cr, Cho, mI) metabolite in the j-th (j-1…480) voxel in the k-th (k-1…18) subject, Sijk, were estimated from their peak area using the freely available SITools-FITT spectral modeling software of Soher et al. (24 (link)). It used the full lineshapes of aspartate, glutamate, glutamine, Cho, Cr, mI, NAA and taurine as model functions obtained with the GAVA simulation program for our pulse sequence (25 ). This process, which takes about 30 minutes, uses a priori spectral information and includes non-parametric baseline signal components characterization and Lorenz-Gauss lineshape assumption. Analysis of this baseline modeling showed that for spectra with 5 Hz linewidth, the mean errors of the fit are 3.4%, 2.3% and 2.8% for NAA, Cr and Cho (26 (link)). The Sijk-s were scaled into absolute amounts, Qijk, against a 2 L sphere of Civitro=12.5, 10.0, 3.0 and 7.5 mM NAA, Cr, Cho and mI in water at physiological ionic strength to load the coil and VOI size and position similar to the in vivo studies to approximate a similar B1 profile up to the intrinsic differences between the phantom and the head due to tissue – RF field interactions at 3 T:
Qijk=CivitroV⋅SijkSijR⋅(Pk180°PR180°)1∕2⋅fimillimoles,
where V is the voxel volume (0.75 cm3), SijR the sphere voxels' metabolites' signal, Pk180° and PR180° are the RF power for a non-selective 1 ms 180° inversion pulse on the k-th subject and reference. To account for different relaxation times in vivo (T1vivo, T2vivo) and in the phantom (T1vitro, T2vitro), the Qijk in were corrected for each metabolite i with (27 (link)):
fi=exp(−TE∕T2vitro)exp(−TE∕T2vivo)⋅1−exp(−TR∕T1vitro)1−exp(−TR∕T1vivo).
Tal A., Kirov I.I., Grossman R.I, & Gonen O. (2012). The Role of Gray and White Matter Segmentation in Quantitative Proton MR Spectroscopic Imaging. NMR in biomedicine, 25(12), 1392-1400.
Publication 2012
Aspartate FITT Glutamate Glutamine Head Inversion, Chromosome MS 180 physiology Proton Magnetic Resonance Spectroscopy Pulse Rate Taurine Tissues

Most recents protocols related to «FITT»

1
Home-based Virtual Pulmonary Rehabilitation Protocol

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Publication 2023
Ankle Behavior Therapy Conclude Resin Diabetes Mellitus Disease Progression FITT Infection Injuries Leg Long Terminal Repeat Lower Extremity Lung Transplantation One-Step dentin bonding system Oxygen Saturation Preventive Health Programs Pulse Rate Rate, Heart Safety Self Confidence Signs, Vital Wrist
2
Comprehensive Systems Review for Down Syndrome
Starting with the ‘Body Structure and Function’ of the ICF model, a literature search was performed to support a physical therapy-based ‘systems review’, with an overview of ‘Impairments’ that impact ‘Activities’ (see Figure 1) for individuals with Ds. This ‘systems review’ is the framework that physical therapists use to examine their patients [19 (link),25 (link)]. It organizes impairments in cardiovascular/pulmonary, integumentary, musculoskeletal, and neuromuscular systems, as well as the patient’s cognitive, language, and learning abilities. We searched the following databases between December 2021 and December 2022: Pubmed, Embase, and CINAHL. We used wide search terms referring to these systems combined with ‘Down syndrome’ or ‘trisomy 21’. We further used the review papers that resulted from this search to find original papers and used reference lists of all papers to find other applicable work; however, the literature was scarce on many of the systems and/or impairment for adults with Ds. In this paper we have narrowed our focus to impairments in individuals with Ds directly impacting physical activities, but for possible interventions we had to expand our search to other populations for some impairments. For a more comprehensive review of co-morbidities in Ds, we refer to two recent reviews of the literature by Capone et al. [7 (link)] or earlier overview papers, such as Bittles et al. [26 (link)]. We translated these findings into recommendations for both content and delivery of an exercise program for individuals with Ds, organized by the same categories of the systems review described above. Recommendations for the content of the program will be discussed according to the FITT criteria formulated by the American College of Sports Medicine: Frequency, Intensity, Time, and Type [27 ]. Finally, a detailed exercise program based on these recommendations is presented.
Mann S., Spiric J., Mitchell C, & Hilgenkamp T.I. (2023). Development of a Physical Therapy-Based Exercise Program for Adults with Down Syndrome. International Journal of Environmental Research and Public Health, 20(4), 3667.
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Publication 2023
Adult Cardiovascular System Cognition Down Syndrome FITT Human Body Lung Obstetric Delivery Patients Physical Therapist Population Group System, Integumentary Therapy, Physical
3
Evaluating Physical, Functional, and Quality of Life Outcomes in Older Adults
Validated scales and tests were used to assess various client outcomes at baseline (T0). The Phone-FITT scale [16 (link)] (frequency + duration FD summary score) was used to measure household and recreational activities, with higher total FD scores indicating greater participation in that category of physical activity. The KATZ Activities of Daily Living Index [17 (link)] was used to assess a client’s independence to perform six daily activities of living. A summative score ranged from 0–6, in which a score of 6 indicated full function, 4 indicated moderate impairment and ≤2 indicated severe functional impairment. The Iconographic Falls Efficacy Scale [18 (link)] was used to assess client’s fear of falling by using ten pictures to describe a range of activities and situations. A summative score ranged from 10–40 in which a score of 10–18 indicated low concern about falling and 19–40 indicated high concern about falling. The QoL-AD [19 (link)] has 13 items that measure quality of life in people with dementia. A summative score is in the range of 13–52, in which a higher score indicated a better quality of life. The functional mobility of clients was assessed using the Time-Up-and-Go test [20 ] at a maximal but safe speed, whereby a time ≥12 s indicated the person was at risk of falling. Lower limb functioning was evaluated using the Short Physical Performance Battery test [21 (link)] which consisted of balance tests (side-by-side stand, semi-tandem stand and tandem stand), a gait speed test (time to walk a 3-metre path, with the use of a walking aid if needed), and a chair-stand test (time to complete 5 chair stands). Each test category scored 0–4, whereby a higher score indicated better standing balance, higher gait speed and better lower limb strength, respectively. The summative score of all test categories (sum of balance test, gait speed test and chair stand test scores) ranged from 0–12, whereby a score of 12 indicated full function and <10 indicated one or more mobility limitation.
Client and/or carer rated client’s sleep quality in the past three months at baseline (T0) on a 5-point Likert scale. A rating of “1” represented “very good” sleep quality, “3” represented “average” and “5” represented “very poor” sleep quality. Data on carer- and/or client-reported falls in the past month, whereby a fall was defined as “an event which results in a person coming to rest inadvertently on the ground or floor or other lower level” [22 ], and healthcare use in the past three months were also collected.
Lee D.C., Haines T.P., Callisaya M.L, & Hill K.D. (2023). A Scalable Program for Improving Physical Activity in Older People with Dementia Including Culturally and Linguistically Diverse (CALD) Groups Who Receive Home Support: A Feasibility Study. International Journal of Environmental Research and Public Health, 20(4), 3662.
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Publication 2023
Dementia Fear FITT Households Lower Extremity Mobility Limitation Performance, Physical Range of Motion, Articular
4
Safe Functional Home Exercise for Dementia
Validated scales and tests were used to reassess client outcomes at Week 12 (T3) via a second home visit by the chief researcher. The Phone-FITT scale, the KATZ index of independence in activities of daily living, the Iconographic Falls Efficacy scale, the QoL-AD, the Timed-Up-and-Go test, the Short Physical Performance Battery test and the Likert scale of sleep quality in the past 3 months were repeated. Data for carer and/or client reports of healthcare use in the past three months and falls in the past month were collected. In addition, the Phone-FITT scale measures falls, and reports of falls in the past month were also collected at Week 4 (T1) and Week 8 (T2) via telephone contacts. A schema of the implementation and assessment of the “Safe Functional Home Exercise” program for people with dementia is shown in Figure 3.
Lee D.C., Haines T.P., Callisaya M.L, & Hill K.D. (2023). A Scalable Program for Improving Physical Activity in Older People with Dementia Including Culturally and Linguistically Diverse (CALD) Groups Who Receive Home Support: A Feasibility Study. International Journal of Environmental Research and Public Health, 20(4), 3662.
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Publication 2023
FITT Performance, Physical Presenile Dementia
5
Prehabilitation for Gastroesophageal Cancer
In accordance with World Health Organization guidelines, a personalised home-based program was prescribed by an exercise therapist, including a moderate-intensity activity with a minimum duration of 150 min. A tailored exercise programme consisting of aerobic and strength exercises was prescribed based on FITT (frequency, intensity, time and type) principles.
Every week, there was a scheduled telephone touch point with an exercise therapist. When exercise goals were achieved, the exercise programme was progressed according to FITT principles. For those who were unable to meet their goals, the programme was adapted to their clinical condition and re-evaluated at the next touchpoint. Dietetic support was provided by a specialist dietitian who undertook an assessment of nutritional status, including identification and stratification of nutritional risk. A plan was agreed based on symptoms, dietary eating habits and nutritional deficiencies. Weekly or fortnightly phone calls from the dietitian were used to monitor adherence to the programme. Interventions, such as oral supplementation or enteral feeding via a jejunostomy, were established when risk was identified. Psychometric screening was completed for all patients and psychological support was provided by a clinical nurse specialist trained in Level 2 psychological interventions.
The overall aim was to explore and address anxieties or concerns the patient may have regarding their diagnosis, symptoms and/or treatment plan, facilitate adaptation to their current psychological health and disease state and improve self-efficacy.
Motivational interviewing techniques were used by all professionals to identify any potential barriers or facilitators to adherence and facilitate positive behaviour change. This was accompanied by a timeline of agreed goals with personalised written and visual information.
The prehabilitation programme started at the point of diagnosis, once a decision to proceed with curative resection had been made, and continued throughout NAC until the time of surgery. All patients at centre A who underwent surgical resection with curative intent were eligible to participate in the prehabilitation program.
Centre B did not provide prehabilitation. There were no other significant differences in pre-operative care, other than the provision of prehabilitation. Dietetic support in centre B is consistent with national guidelines and consists of an initial assessment and identification of risk followed by further interactions only if there is any deterioration in status.
In both centres, the same chemotherapy and chemo-radiotherapy regimes were used. Patients who required chemotherapy received either 3 cycles each of Epirubicin, Cisplatin and Capecitabine (ECX) or Epirubicin, Oxaliplatin and Capecitabine (EOX) or 4 cycles of Fluorouracil, Leucovorin, Oxaliplatin and Docetaxel (FLOT). Oncologists in both centres attend the weekly specialist multi-disciplinary meeting and work to similar protocols in terms of choice of chemotherapy regimen and clinical behaviours, such as tailoring of the regimen to each individual patient, dose reduction, treatment cessation, etc.
Christodoulidis G., Halliday L.J., Samara A., Bhuva N., Park W.H, & Moorthy K. (2023). Personalized Prehabilitation Improves Tolerance to Chemotherapy in Patients with Oesophageal Cancer. Current Oncology, 30(2), 1538-1545.
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Publication 2023
Acclimatization Anxiety Capecitabine Cisplatin Clinical Nurse Specialists Diagnosis Diet Dietitian Docetaxel Drug Tapering Epirubicin Exercise, Aerobic FITT Fluorouracil Jejunostomy Leucovorin Malnutrition Mental Health Nutrition Assessment Oncologists Operative Surgical Procedures Oxaliplatin Patients Pharmacotherapy Prehabilitation Preoperative Care Psychometrics Radiotherapy TimeLine Touch Training Programs Treatment Protocols Withholding Treatment

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More about "FITT"

The FITT framework (Frequency, Intensity, Time, and Type) is a widely recognized approach for designing and evaluating exercise programs.
This comprehensive protocol helps ensure physical activity regimens are tailored to individual needs and desired outcomes, such as improved cardiovascular health, muscle strength, or weight management.
Frequency refers to how often exercise is performed, while Intensity denotes the level of effort or difficulty.
Time encompasses the duration of each exercise session, and Type indicates the specific activities involved (e.g., aerobic, strength training, flexibility).
Leveraging the FITT framework can enhance the effectiveness and consistency of exercise interventions, leading to better research outcomes and improved participant results.
This structured approach is particularly useful for studies involving Furosemide, Escherichia coli strain DH5α, Fura-2/AM, and other related topics.
Researchers can utilize tools like MATLAB 2015b, SPSS version 22.0, SPSS version 23, and SAS 9.4 to analyze and optimize FITT-based exercise protocols.
Additionally, innovative solutions like PubCompare.ai's AI-powered FITT protocol optimization can streamline the workflow and enhance the accuracy of research outcomes.
By incorporating the FITT framework and related concepts, exercise programs can be tailored to individual needs and desired outcomes, ultimately leading to more effective and consistent results.
Remember, even experts can occasionally make a typo, so don't be too hard on yourself if you spot one in this text.